At Zeta Global, identity is at the core of our marketing cloud. The Identity team builds and operates backend services that power the Zeta Identity Graph - collecting, resolving, and maintaining billions of behavioural, demographic, and transactional signals. These services enable people-based marketing at scale, ensure trust and transparency in how identity data is managed, and provide critical APIs and event streams consumed across Zeta’s platform. Our systems support more than 235 million consumer profiles and process billions of events monthly, requiring services that are highly available, low-latency, and built to scale.

We are looking for a Lead Software Engineer who brings strength in both backend distributed systems and data engineering. In this role, you’ll design APIs and event-driven services, scale our systems to handle rapidly growing data volumes, and modernize our platform for resilience, transparency, and performance.

You Will:

• Design and build backend services and APIs for identity resolution, graph transparency, and integrations.
• Develop and optimize event-driven pipelines and workflows processing billions of signals.
• Deploy and operate cloud-native services on EKS/Kubernetes, leveraging Helm for repeatable deployments.
• Ensure reliability, observability, and scalability across services and data pipelines.
• Collaborate across engineering, product, and data teams to deliver end-to-end identity solutions.
• Mentor engineers and influence architecture and roadmap decisions.

You Have:

• 7+ years of experience in backend and/or data engineering at scale.
• Strong skills in Python, Java, or Scala, with expertise in APIs, distributed systems, and event-driven design.
• Hands-on experience with Kafka (or similar), Spark/Hive/Flink, and workflow schedulers (Airflow/Luigi).
• Proficiency with Kubernetes (EKS preferred) and deployment practices (Helm, service meshes).
• Solid knowledge of SQL, data modelling, and cloud platforms (AWS).
• Familiarity with columnar / NoSQL databases (Aerospike, Snowflake, HBase, Scylla, Elasticsearch)
• Knowledge of performance optimization and resilience patterns (caching, retries, fault tolerance).
• Familiarity with CI/CD and automated testing practices for backend services.
• Strong communication skills and a track record of driving technical direction and mentoring teams.

Bonus Skills:

• Experience with large-scale or multi-tenant distributed systems.
• Exposure to real-time streaming frameworks (Flink, Storm).
• Knowledge of open table formats (Iceberg, Hudi, Delta Lake).
• Background in security, compliance, or privacy related to identity systems.

Company Summary:

Zeta Global is a NYSE listed data-powered marketing technology company with a heritage of innovation and industry leadership. Founded in 2007 by entrepreneur David A. Steinberg and John Sculley, former CEO of Apple Inc and Pepsi-Cola, the Company combines the industry’s 3rd largest proprietary data set (2.4B+ identities) with Artificial Intelligence to unlock consumer intent, personalize experiences and help our clients drive business growth.

Our technology runs on the Zeta Marketing Platform, which powers ‘end to end’ marketing programs for some of the world’s leading brands. With expertise encompassing all digital marketing channels – Email, Display, Social, Search and Mobile – Zeta orchestrates acquisition and engagement programs that deliver results that are scalable, repeatable and sustainable.

Zeta Global is an Equal Opportunity/Affirmative Action employer and does not discriminate on the basis of race, gender, ancestry, color, religion, sex, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law.

Zeta Global Recognized in Enterprise Marketing Software and Cross-Channel Campaign Management Reports by Independent Research Firm

https://www.forbes.com/sites/shelleykohan/2024/06/19/amazon-partners-with-zeta-global-to-deliver-gen-ai-marketing-automation/

https://www.cnbc.com/video/2024/05/06/zeta-global-ceo-david-steinberg-talks-ai-in-focus-at-milken-conference.html

https://www.businesswire.com/news/home/20240904622808/en/Zeta-Increases-3Q%E2%80%9924-Guidance

https://www.prnewswire.com/news-releases/zeta-global-opens-ai--data-labs-in-san-francisco-and-nyc-300945353.html

https://www.prnewswire.com/news-releases/zeta-global-recognized-in-enterprise-marketing-software-and-cross-channel-campaign-management-reports-by-independent-research-firm-300938241.html

We invite you to explore this opportunity to be a part of the Zeta family……

Should you need any assistance or require further information, please do not hesitate to contact us.

We’re looking for a Senior Data Engineer to design, build, and operate the data pipelines and aggregates that power Zeta’s AdTech platform. This is a hands-on individual contributor role focused on high-scale batch + streaming data processing, reliable data products, and analytics-ready datasets that enable prediction, agentic workflows, BI reporting, and measurement. You will partner closely with backend, ML, and product teams to deliver trusted, well-modeled data with strong performance, quality, and observability.

What You’ll Do

• Build data pipelines: Develop robust batch and streaming pipelines (Kafka/Kinesis) to ingest, transform, and enrich large-scale event data (impressions, clicks, conversions, costs, identity signals).
• Create data aggregates & marts: Design and maintain curated aggregates and dimensional models for multiple consumers—prediction models, agents, BI dashboards, and measurement workflows.
• Data modeling & contracts: Define schemas, data contracts, and versioning strategies to keep downstream systems stable as sources evolve.
• Data quality & reliability: Implement validation, anomaly detection, backfills, and reconciliation to ensure completeness, correctness, and timeliness (SLAs/SLOs).
• Performance & cost optimization: Optimize compute/storage for scale (partitioning, file sizing, incremental processing, indexing), balancing latency, throughput, and cost.
• Orchestration & automation: Build repeatable workflows with scheduling/orchestration (e.g., Airflow, Dagster, Step Functions) and CI/CD for data pipelines.
• Observability for data systems: Instrument pipelines with metrics, logs, lineage, and alerting to accelerate detection and root-cause analysis of data issues.

Security & governance: Apply least-privilege access, PII-aware handling, and governance controls aligned with enterprise standards.

5+ years building production data pipelines and data products (batch and/or streaming) in a high-scale environment.

Strong experience with SQL and data modeling (dimensional modeling, star/snowflake schemas, event modeling).

• Hands-on experience with streaming systems (Kafka preferred) and/or AWS Kinesis, including event-driven designs.
• Proficiency in one or more languages used for data engineering (Python, Java, Scala, or Go).
• Experience with distributed data processing (Spark, Flink, or equivalent) and performance tuning at scale.
• Experience with AWS data services and cloud-native patterns (S3, Glue/EMR, Athena, Redshift, etc. as applicable).
• Familiarity with lakehouse/table formats and large-scale storage patterns (e.g., Parquet; Iceberg/Hudi/Delta are a plus).
• Experience with orchestration/workflow tooling (Airflow/Dagster/Step Functions) and CI/CD for data workloads.
• Strong data quality/observability practices (tests, monitoring, lineage; understanding of SLAs/SLOs).
• Experience with SQL + NoSQL data stores (e.g., Postgres/MySQL; DynamoDB/Cassandra/Redis) and choosing the right store per use case.

Clear communicator and collaborator; able to work with mixed audiences and translate needs into reliable data interfaces.

• AdTech / programmatic advertising domain knowledge: DSP/SSP/exchange/RTB concepts and data flows.
• Experience building measurement pipelines (attribution, incrementality, lift, or experimentation analytics).

Experience supporting ML feature stores, offline/online feature generation, or model training datasets.

• Experience with real-time analytics stores (Druid/ClickHouse/Pinot) and high-cardinality aggregation strategies.
• Deep knowledge of data governance/privacy, including PII handling and consent-aware data processing.
• Open-source contributions, publications, or conference speaking.
• BS/MS in CS/Engineering or equivalent practical experience.

WHO WE ARE 

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.  

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/ 

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.

Now, more than ever, the Toast team is committed to our customers. We’re taking steps to help restaurants navigate these unprecedented times with technology, resources, and community. Our focus is on building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. And because our technology is purpose-built for restaurants, by restaurant people, restaurants can trust that we’ll deliver on their needs for today while investing in experiences that will power their restaurant of the future. 

Bready* to make a change?

Toast is looking for a Staff Machine Learning Engineer to serve as a technical linchpin for our AI Platform team. At the P4 level, you aren't just deploying models; you are designing the fundamental infrastructure that enables dozens of teams to build, deploy, and monitor AI at scale. You will act as a force multiplier, mentoring senior engineers and setting the architectural standards for our MLOps lifecycle—from feature stores and automated retraining to high-performance inference at the edge.

About this Roll*:

• Architectural Leadership: Design and lead the evolution of a unified MLOps platform that supports diverse needs across Toast, ensuring high availability, scalability, and security of ML services.
• Engineering Excellence: Champion and institutionalize best practices for CI/CD for ML (MLOps), automated testing, and infrastructure-as-code (Terraform).
• Cross-Functional Synergy: Lead collaborative efforts across Data Engineering, DevOps, and Product teams to bridge the gap between model prototyping and production-grade reliability.
• Strategic Roadmapping: Partner with leadership and Product Managers to define the 1-2 year technical vision for AI infrastructure, prioritizing long-term stability over short-term fixes.
• Operational Ownership: Set the standard for observability and incident response for ML systems, driving root-cause analysis for complex system failures.
• Mentorship: Actively mentor P2 and P3 engineers, fostering a culture of technical rigor and continuous learning.

Do you have the right ingredients*?

• Education: Bachelor’s or Master’s degree in Computer Science, AI, or a related technical field.
• Experience: A minimum of 10-12+ years of professional software engineering experience, with at least 6-7 years specifically focused on productionizing and scaling ML systems at the enterprise level.
• Core Tech Stack: Expert-level proficiency in Python, Scala, or Java/Kotlin. Extensive experience with PySpark and high-performance computing.
• Generative AI & LLMOps: Proven track record of taking LLM applications from research to production, including experience with Vector Databases, LangChain/LangGraph, and A2A protocols.
• System Design: Superior ability to design distributed systems that handle millions of requests with sub-second latency.
• Experience with microservice based architecture, preferably with AWS tooling (SageMaker, DynamoDB, Athena, Glue, etc.)
• Experience in software engineering best practices and tools including object-oriented programming, test-driven development, CI/CD, git, shell scripting, task orchestration, MLflow
• Profound knowledge of model deployment, orchestration (Apache airflow, Prefect), scaling, and managing CPU/GPU resources efficiently. 
• Exceptional problem-solving, analytical skills and the ability to tackle complex problems with a critical thinking approach. 
• Outstanding communication and interpersonal skills, coupled with a demonstrated ability to work collaboratively within a team environment.
• Foundational knowledge in statistical concepts (e.g. classification, regression, etc) and deep learning algorithms (e.g. CNN, RNN) is desirable

Bonus Ingredients (The "Staff" Edge)

• Data Strategy: Experience implementing enterprise-grade Feature Stores (e.g., Tecton, Feast) and real-time streaming frameworks like Apache Flink or Ray.
• Full-Stack Visibility: Ability to dive into the UI/UX layer (React) or deep into the kernel/networking layer to debug performance bottlenecks.

Open Source/Community: Contributions to relevant open-source projects (MLflow, Kubeflow, etc.) or a history of speaking at industry conferences.

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy 
We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

The Community You Will Join:

The CRM Engineering team builds world-class tools that drive growth and foster meaningful relationships between Airbnb and its community, including Hosts, Guests, Law Enforcement, Government Regulators, Local Community Leaders, and more. This team tackles some of the most challenging and unique problems faced by our business. Here are a few examples:

• Growth Platform: Designed to increase supply and retain existing hosts.
• Policy Platform: Enables Airbnb to engage with regulatory bodies across the globe regarding short-term rental regulations. This platform tracks regulations that impact the Airbnb business and provides intelligence to address unique business challenges.
• Community Platform: Establishes a social platform to drive community enablement, collaboration, and success.
• Agent Tools: Empowers agents to understand and resolve issues that customers are unable to address on their own.
• Aircover Claims Platform: Facilitates the processing of claims for Aircover insurance products, offering protection to both hosts and guests.

The Difference You Will Make:

The Community Platform team is building a groundbreaking social platform aimed at delivering a differentiated service to our community. This platform will empower local communities to collaborate, support community leaders, host local and global events, provide improved resources, drive engagement, mobilize the community, and offer direct feedback to Airbnb.

We are seeking a highly skilled and motivated Full Stack engineer who is passionate about making a difference through their work. You will play a significant role in shaping the technical vision and then delivering a solution that is flexible, efficient and scales with the needs of the business. Each individual brings their own unique skill set, experiences, thought leadership and technical expertise to solve these technical challenges for Airbnb.

A Typical Day: 

• Work cross-functionally with product, design, and other engineering counterparts to execute on product and business strategy to build novel products and features
• Drive, lead, and contribute to the full development cycle: technical design, development, test, experimentation, analysis, and launch.
• You’ll be reviewing code and design docs, giving feedback on product specs and mocks.
• Provide critical input in team roadmap and technical direction.

Your Expertise:

• 6+ years of full software development experience
• Proficient in at least one major programming language (preferably Java / Kotlin / Scala / Ruby on Rails / Python).
• Passion for delivering products end-to-end: from ideation through planning and scoping to implementation and experimental A/B testing.
• Exposure to architectural patterns of large, high-scale web applications, such as well-designed APIs, high volume data pipelines, and efficient algorithms.
• Passionate about design and building UX products. You bring flexibility to collaboration with design and product partners by presenting tradeoffs and iterating to the ideal solution.
• Outstanding communication and interpersonal skills and detail oriented.
• Bachelor’s and/or Master’s degree, preferably in CS, or equivalent experience.

Smartsheet is hiring a Senior Machine Learning Operations Engineer to architect our machine learning production lifecycle. Your mission is to maintain and deploy ML models to a scalable, reliable, and secure production environment. You will design and maintain the infrastructure, automation, and monitoring systems that ensure our AI products are high-performing and cost-effective.

You will report to our Director, Analytics Engineering & Data Governance and work from our Bangalore, India office. 

You Will:

Model and Pipeline Automation 

• Automate the deployment and retraining of ML models, from training through to production inference, by building and managing complete CI/CD/CT (Continuous Training) pipelines, adhering to MLOps best practices. 
• Build, fine-tune, or use pre-trained LLMs, deep learning models or traditional machine learning models.
• Evaluate and recommend AI or ML solutions for the product using any combination of vendor solutions and/or custom-built models.

Governance & Compliance

• Implement model versioning, lineage tracking, and auditing to ensure compliance with security and ethical standards.

Performance Monitoring

• Continuously monitor the health and performance of production machine learning models, proactively identifying and correcting model drift, staleness, and performance degradation. 
• Incorporate user feedback for iterative improvements and manage necessary model retraining cycles.

Cross-Functional Collaboration

• Act as the "glue" between Data Scientists (who build models) and Software Engineers (who consume them).
• Partner effectively with software engineers, product managers and business functions to integrate the machine learning solutions across smartsheet.

Architecture and Infrastructure Management

• Provision and manage scalable cloud infrastructure using Infrastructure as Code (IaC).
• Provide architectural guidance and mentorship to a team consisting of ML engineers, data scientists and analytics engineers.
• Distill complex ML concepts into easy-to-follow technical documentation.

You Have:

• 5+ years of experience with creating, deploying and scaling machine learning solutions in a cloud environment (eg. AWS, GCP, Azure) and ability to use tools such as SageMaker, Glue, Lambda, Docker etc. to create ML models and data pipelines. 
• 7+ years of programming experience in languages used in AI/ML (eg python, scala etc)
• 4+ years of experience in developing deep learning and traditional ML models using common frameworks like pytorch, tensorflow, huggingface, scikit-learn etc.
• Strong applied data science skills - ability to recognize data patterns, understand how and when to use various machine learning approaches (eg. supervised/unsupervised learning, deep learning etc.), and evaluate the performance of ML algorithms. 
• Proven ability to remain up-to-date with the latest advancements in Generative AI approaches (eg. OpenAI, LangChain, Stable Diffusion APIs).
• Experience developing, documenting, and supporting REST APIs
• A degree in Computer Science, Engineering, or a related field or equivalent practical experience.

Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Principal Data & AI/ML Ops Engineers, who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform. 

You Will:

• Data Architecture and Design: Designing and overseeing the architecture of scalable and reliable data platforms, including data pipelines, storage solutions, and processing systems
• Data Modelling and Management:Developing and implementing data models, ensuring data quality, and establishing data governance policies
• Data Pipeline Development: Building and optimising data pipelines for ingesting, processing, and transforming large datasets from various sources
• Performance Optimisation: Identifying and resolving performance bottlenecks in data pipelines and systems, ensuring efficient data retrieval and processing
• Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure
• Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform
• Data Security and Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data
• Perform other duties as assigned

You Have:

• Enterprise SaaS software solutions with high availability and scalability
• Solution handling large scale structured and unstructured data from varied data sources
• Experience in building and maintaining data platform systems such as distributed compute, data orchestration, distributed storage, streaming infrastructure ensuring scalability, reliability, efficiency and security
• Working with Product engineering team to influence designs with data, AI and analytics use cases in mind
• In depth experience in System design involving large Petabytes of data with Databricks Lakehouse
• Experience in modern AI/Data infrastructure patterns, Semantics layer Organizing data for AI agents (metadata, context)
• AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph
• Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
• Programming languages like Python, SQL, and potentially Java or Scala
• Exposure to Snowflake and Data pipeline frameworks like Airbyte/Airflow is preferable
• Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting
• Solution Cost Optimisations and design to cost
• Driving engineering excellence initiatives
• Legally eligible to work in India on an ongoing basis

Why This Role Matters 

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.  

What Makes This Opportunity Unique 

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue.  You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models . 

Responsibilities 

• Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking. 

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics). 

• Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction. 

• Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning. 

• Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval. 

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows. 

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging. 

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance. 

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems. 

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines. 

• Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage. 

• Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost. 

• Run continuous offline/online evaluations for quality, factuality, bias, and safety for the platform sanity. 

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls. 

• Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support compliances where needed. 

• Provide templates/CLIs, sandboxes, and docs so product teams can build and ship fast; mentor engineers and champion MLOps and AI safety best practices. 

Desired Skills & Experience 

• 12–15+ years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems). 

• Production experience with Python plus one of Java/Scala/Go; microservices and API design. 

• MLOps at scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka). 

• Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS. 

• Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows. 

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting. 

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry. 

• Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph), GraphQL, vector search (e.g., pgvector/Qdrant/Milvus), hybrid retrieval patterns. 

Preferred Mindset 

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs. 

• Thinks in systems - observability, fallback, access control are core, not afterthoughts. 

• Passionate about AI - enjoys enabling real-world LLM and agentic use cases. 

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation. 

• Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience. 

• Documentation and teaching: you make complex systems understandable; you uplevel teams. 

This Software Engineer 3 role will work on advancing one of the Black Duck Product.

The ideal candidate will have knowledge of Full Stack development - Scala and react (Must) to help us create web-based tools that aids developers in writing better, more-secure software. You will play an effective role in the full application development lifecycle. You will work with customers to understand their needs, maintain the latest tools and techniques for software security, shape projects, define tasks, and build solutions. This is a hands-on role for a person with solid debugging and communication skills, and a keen focus on helping our users write better code.

Key Qualifications: 

• 6+ years of experience in software development building enterprise-grade customer facing products
• Proven experience working on web-based applications with Scala and React
• Proficiency with Scala (including Cats and/or Shapeless, Monix, sbt), Java (Optional), JavaScript/React, HTML/CSS, REST, and SQL
• Experience with using a RDBMS (MariaDB/MySQL preferred) for storing and queuing large sets of data
• Experience with Git, GitOps, Jira, and Confluence or equivalent tools
• Stellar written and oral communication skills
• Customer-first and results-oriented mindset and the ability to make practical decisions when making engineering choices
• BS/MS in Computer Science/Software Engineering or related discipline

Key Responsibilities: 

• Work on architecture, design, and product definition that drives influential decisions and outcomes related to project direction
• Has a team-player mentality - can communicate effectively with all team members
• Attracted to learning new technologies and expanding existing software to meet the changing needs of our key demographics
• Able to communicate complex material to technical and non-technical audiences
• Develop high quality, deliverable software with little management intervention or oversight

Location : Bangalore
About Team & About Role:

About the role : 

The Principal Engineer role is a highly strategic position, operating at the same level as an Engineering Director. In the Developer Platform organization, you will be responsible for defining and executing the technical strategy for multiple key systems/technical areas that are of significant impact to the company's Software Development Lifecycle (SDLC), developer experience, and core engineering productivity.

The scope and impact of your work will span the interactions among multiple complex systems and engineering organizations, focusing on the end-to-end flow of software delivery, scaling Developer Platforms & Infrastructure, and leveraging AI to revolutionize productivity. You will sit at the center of driving these critical improvements, ensuring the platform scales to meet the company's growth.

Your primary focus will be problem seeking. This involves not only defining plans for ambiguous problems (e.g., "How do we scale our platform 10x while reducing costs?") but also identifying problems crucial for the future of engineering productivity and proposing plans for their resolution, such as the strategic integration of AI/ML into core developer workflows.

What you’ll do

Your responsibilities will encompass technical leadership, strategic delivery and people growth with a focus on cross-functional impact across the Developer Platform and the wider Engineering organization:

• Platform Strategy and AI-Powered Productivity
• Define the long-term technical vision (3+ years) for the Developer Platform, including core infrastructure, CI/CD pipelines, and observability systems, ensuring they are cohesive and sustainable.
• Pioneer the integration of AI/ML not just for engineering tasks but to drive overall company productivity, transforming internal processes and tools.
• Identify and translate a future business opportunity or engineering productivity gap into a solvable, multi-quarter technical problem with a clear roadmap.
• Drive significant improvements in engineering velocity and quality by identifying and solving bottlenecks within the software development process and continually introducing innovation.
• Technical Leadership and Standards
• Apply domain expertise across multiple system domains (e.g., CI/CD, Observability, Cloud Infrastructure, Tooling) to drive future-proof, high-leverage platform solutions.
• Set standards for quality, structure, and operational excellence across all core platform systems.
• Be a  Consensus Builder, resolving conflicting priorities and mitigating risks across dependent engineering teams.
• Take end-to-end ownership of complex, cross-cutting projects (10-18 engineers' effort) from inception to operation.
• Engineering Influence & Growth:
• Drive and influence collaboration across the entire Engineering organization, establishing a unified software development vision.
• Act as a company ambassador through significant external contributions (e.g., open-source, publications, standards committees).
• Master the craft of coaching, mentoring Staff Engineers for Principal-level impact, and raising the hiring bar

About You 

• Typically 18+ years of industry experience in Software Engineering, with a significant focus on Developer Platforms, Infrastructure, or Core Tools.
• Demonstrated ability to function at a level equivalent to an Engineering Director in terms of strategic impact, scope of influence, and technical ownership.
• A proven track record of identifying, defining, and driving the technical strategy for high-impact initiatives across multiple complex, multi-organizational systems.
• Deep expertise in a general-purpose programming language (e.g., Go, Java, C/C++, Scala, Python) and mastery in applying first principles thinking—actively questioning assumptions to create new knowledge and solutions for core engineering problems.
• Exceptional skills as a Consensus Builder and Cross-Functional Leader, adept at navigating conflicting priorities and influencing stakeholders across various teams to achieve a unified vision.
• Experience in architecting and scaling highly available, fault-tolerant Developer Platforms and Infrastructure.
• A demonstrable understanding of how to strategically leverage AI/ML to improve software development processes and overall company productivity.
• Ability to act as an effective coach and mentor, supporting the growth of engineers up to the Staff level.
• Capable of self-directing, managing time, and advocating for resources for activities that are critical for the company's future engineering velocity and platform stability.

About the Team & Role

The platform security team is responsible for building secure, scalable infrastructure and platform services that power Rubrik's enterprise data protection solutions. We are seeking talented software engineers who are passionate about cloud security, possess deep technical expertise in securing cloud infrastructure, and stay current with industry-leading security practices for cloud-native architectures.

As a Staff Software Engineer, you will design, develop, and maintain scalable and resilient frameworks and services for all cloud infrastructure supporting Rubrik products. This is an excellent opportunity for engineers who are passionate about cloud platform security and want to make a meaningful impact on how enterprise data protection operates in the cloud. You will get to work on cutting-edge cloud security infrastructure and frameworks that define the future of Rubrik's cloud platform roadmap. Along the way, you'll help solve problems such as:

• How to build extensible, secure frameworks for deploying cloud infrastructure?
• How to implement zero-trust networking principles in Kubernetes and cloud environments?
• How to build secure CI/CD pipelines and infrastructure-as-code with security guardrails?
• How to design secure service mesh architectures for microservices communication?
• How to implement secure secrets management and credential rotation at scale?
• How to build secure container orchestration with runtime security monitoring?

In addition to solving engineering problems like these, you will also collaborate with partner teams such as Product Management, SRE, DevOps, Infrastructure, and other engineering teams. We need our engineers to be versatile, display leadership qualities, and be enthusiastic to take on new problems across the cloud infrastructure stack as we continue to push technology forward. With your technical expertise, you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance secure cloud platform solutions.

This is an excellent opportunity for those looking to build a secure cloud infrastructure from the ground up. The problem space is wide (covering the entire Rubrik cloud portfolio) and deep (from performant core infrastructure to secure service architectures), and has an exciting roadmap for the coming years with tangible business impact. You'll work alongside distinguished platform and security engineers who will mentor you as you grow your technical skills and cloud security domain knowledge.

What You'll Do:

Secure Cloud Platform Development:

• Collaborate with product managers, architects, SRE, and cross-functional teams to define security standards for cloud infrastructure, implement the frameworks, and drive the migration. 
• Take full ownership of projects from design to implementation, test and deployment of secure cloud infrastructure components
• Write clean, well-tested code in languages like Go, Python, or Scala for cloud infrastructure frameworks. 
• Implement security-first cloud platform features following established patterns and best practices
• Collaborate with senior engineers on secure system design and cloud architecture decisions.
• Triage platform security issues and debug/track/resolve them by analyzing the sources of issues and driving the resolution
• Demonstrate ownership and accountability for delivering secure, reliable platform services.
• Identify security vulnerabilities in cloud infrastructure and build solutions to harden platform services
• Create/Update highly effective documentation to enable other developers and platform teams

Cloud Security Architecture & Reviews:

• Review infrastructure-as-code, container configurations, and cloud architecture designs for security concerns
• Ensure secure cloud engineering standards are followed across the organization
• Contribute to cloud security guidelines and documentation for other engineering teams
• Evangelize secure cloud practices across all platform and engineering teams
• Perform threat modeling for cloud services and infrastructure components

Team Collaboration:

• Lead by example and demand high-quality, secure deliverables from others
• Work with Product Management and SRE to understand cloud platform security standards and requirements
• Collaborate with other engineering teams on integrations with secure frameworks for cloud infrastructure.
• Participate in team meetings, planning sessions, and knowledge sharing
• Coach and mentor junior team members for their professional growth in cloud security engineering

Learning & Growth Opportunities:

• Work closely with other staff and distinguished engineers who will mentor your development in cloud security
• Gain hands-on experience with cloud security services, container security, and infrastructure security
• Learn about cloud threat modeling and secure cloud architecture principles
• Participate in cloud security training and knowledge-sharing sessions
• Contribute to a positive, learning-focused team culture

What We're Looking For

Required Qualifications:

• Bachelor's degree in Computer Science, Software Engineering, or related technical field, or equivalent practical experience
• 10+ years of software development experience with a focus on cloud platforms and infrastructure
• Strong experience with cloud platforms (AWS, Azure, GCP), including security services and best practices
• Deep understanding of cloud security principles, container security, and infrastructure security
• Proficiency in infrastructure-as-code tools (e.g., Terraform)
• Experience with container orchestration platforms (Kubernetes, Docker) and their security models
• Strong programming skills in at least one language (Go, Python, Java, Rust, or similar)
• Experience with cloud-native security tools and practices (service mesh, zero-trust networking, secrets management)
• Understanding of networking security concepts (VPCs, security groups, network policies, TLS/mTLS)
• Experience with version control systems (Git) and collaborative development
• Passion for cloud security engineering - genuine interest in building secure cloud infrastructure
• Proactive mindset - takes initiative to learn new cloud technologies and solve complex infrastructure problems independently

Preferred Qualifications:

• Experience with service mesh technologies (Istio, Linkerd, Consul Connect)
• Hands-on experience with cloud security services (AWS IAM/KMS/GuardDuty, Azure Key Vault/Defender, GCP IAM/KMS)
• Experience with secure CI/CD pipelines and DevSecOps practices
• Familiarity with compliance frameworks (SOC2, FedRAMP, GDPR) and their cloud implementation
• Experience with monitoring and observability tools for cloud security (Prometheus, Grafana, cloud-native monitoring)
• Knowledge of cryptographic protocols and their implementation in cloud environments
• Experience with infrastructure security scanning and vulnerability management
• Understanding of microservices security patterns and API security
• Experience with cloud cost optimization and resource management

Why Join Us?

Perfect Growth Opportunity in Cloud Security Engineering

• Sharpen your cloud security skills by tackling challenging and real-world problems and collaborating with experienced mentors
• Work on meaningful projects that secure enterprise customers' critical data in the cloud
• Gain exposure to cutting-edge cloud security technologies and industry best practices
• Build a strong foundation for a career in cloud platform or security engineering
• Move your career forward with opportunities to specialize in cloud security and leading platform teams

Technical Learning Opportunities

• Hands-on experience with modern cloud security technologies and frameworks
• Exposure to large-scale distributed cloud systems and multi-cloud platforms
• Opportunity to contribute to open-source cloud security and platform projects
• Access to the latest cloud development tools and security research

What Makes a Great Candidate

• Loves to learn - excited about diving into new cloud technologies and security concepts
• Takes initiative - doesn't wait to be told what to do, asks questions, and seeks out learning opportunities in cloud security
• Thinks systematically - can analyze complex cloud infrastructure problems and think through potential security implications
• Collaborates well - enjoys working with others and learning from teammates across platforms and security domains
• Pays attention to detail - understands that small details matter in cloud security and infrastructure reliability
• Is passionate about cloud security - genuinely interested in building secure cloud infrastructure and protecting customer data

The Community You Will Join:

The Guest & Host team based out of India, operates to ensure high quality supply and merchandising differentiates our brand, by providing tools to be used by 1000+ operations agents for supply vetting and quality moderation. As Airbnb expands beyond the core, it is looking to onboard supply outside of the core stays business which requires enhancing our tools. The operational tasks we face are diverse and rapidly evolving. The current manual processes and lack of optimum tooling capabilities hinder the ability of operation agents to work more efficiently, maintain business continuity, and achieve higher scale. 

The Difference You Will Make:

As a Data Engineer on the Guest & Host team, you will play a pivotal role in shaping the systems that onboard and manage the highest quality supply across Airbnb. You’ll build robust data pipelines and platforms that power end-to-end visibility for supply operations, from onboarding and regulatory compliance to AI-assisted vetting and listing quality enforcement. Your work will directly enable operations, product, and ML teams to scale trusted supply globally, ensuring every home, service, and experience meets Airbnb’s standards. Whether it’s surfacing insights to optimize onboarding flows, enabling smarter audit triggers, or supporting nuanced licensing workflows, your contributions will be foundational to helping hosts join Airbnb efficiently and ensuring guests have access to safe, high-quality stays.
As you build and maintain key components of the critical data ecosystem, you’ll have the opportunity to contribute to creating standards and best practices for Airbnb’s Data Engineering. Your work on solving complex business challenges at scale will be instrumental in shaping the tools, processes, and standards used by the broader data community.

A Typical Day:

• Design, build, and maintain robust and efficient data pipelines that collect, process, and store data from various sources, including user interactions, listing details, and external data feeds.
• Develop data models that enable the efficient analysis and manipulation of data for merchandising optimization. Ensure data quality, consistency, and accuracy.
• Build scalable data pipelines (SparkSQL & Scala) leveraging Airflow scheduler/executor framework
• Collaborate with cross-functional teams, including Data Scientists, Product Managers, andSoftware Engineers, to define data requirements, and deliver data solutions that drive merchandising and sales improvements.
• Contribute to the broader Data Engineering community at Airbnb to influence tooling and standards to improve culture and productivity
• Improve code and data quality by leveraging and contributing to internal tools to automatically detect and mitigate issues

Your Expertise:

• 6+ years of relevant industry experience 
• BE/B.tech in computer science or relevant technical field
• Extensive experience designing, building, and operating robust distributed data platforms (e.g., Spark, Kafka, Flink, HBase) and handling data at the petabyte scale.
• Strong knowledge of Java, Scala, or Python, and expertise with data processing technologies and query authoring (SQL).
• Demonstrated ability to analyze large data sets to identify gaps and inconsistencies, provide data insights, and advance effective product solutions
• Expertise with ETL schedulers such as Apache Airflow, Luigi, Oozie, AWS Glue or similar frameworks
• Solid understanding of data warehousing concepts and hands-on experience with relational databases (e.g., PostgreSQL, MySQL) and columnar databases (e.g., Redshift, BigQuery, HBase, ClickHouse)
• Excellent written and verbal communication skills

Job Title: Manager, Software Engineer

Locations: Bangalore

Reports to: VP, AI Engineering

 A quick snapshot…

Conga is looking to hire a Manager, Software Engineering to join our extremely talented AI Engineering team. As an Engineering Manager at Conga, you’ll lead a team of software engineers working on our next generation AI Solutions. This team will be responsible for building statistical and machine learning models that support product features across the company. You’ll get to work directly with other Engineering Managers, Architects, Data Scientists, Product Managers, and Directors, all of whom make up the larger team. In this highly collaborative environment, you will have the opportunity to grow as a leader and help mentor others.

This position requires experience leading software engineering initiatives, knowledge of AI/ML solutions, as well as excellent people and time management skills. This role will be reporting to the VP of AI Engineering and will be a key engineering leader inside the Conga AI Pillar. The role requires you to collaborate with other leaders at Conga and guide your team to deliver high quality software solutions in a timely manner. The ideal candidate for this position is passionate about people, process, technology, and delivering customer values.

Conga works in an agile environment where there are daily stand-ups, code reviews, and constant communication within each self-managed cross-functional team. The ability to communicate effectively with other Engineering and Product leaders, as well as work closely with other your team members are key components for success in this position.

Why it’s a big deal…  

8+ or more years of experience in the Software Engineering industry with a record of increasing responsibility leading software development initiatives, and 3+ years managing teams.

Deep understanding of the Software Development Lifecycle (SDLC) and Machine Learning Lifecycle (MLL).

Lead and drive the strategy, vision and roadmap for building robust, scalable AI/ML foundations and platforms — including but not limited to:

•  Feature stores
• Distributed ML model training
• Model versioning and evaluation
• Monitoring, drift detection, and automated retraining pipelines
• Responsible AI, model explainability, and fairness assessments

Experience with productionizing ML models and integrating them into real-time and batch applications. Work closely with data science partners and domain engineering teams to understand AI/ML use cases and build corresponding foundations for personalization, NLP, forecasting, recommendations, and generative AI.

Experience using a popular programming language such as: Python (preferred), Java, C#, Scala, etc.

Solid understanding of modern software development practices including object-oriented programming, concurrency, design patterns, microservices architecture, RESTful API design, and test-driven development.

Experience with software testing concepts and tools: white/black box testing, coverage analysis, mocking, PyTest/xUnit, Selenium, etc.

Hands-on experience with GCP technologies such as BigQuery, GKE, GCS, DataFlow, Kubeflow, and Vertex AI — or equivalent services in AWS and/or Azure.

Familiarity with ML frameworks and tools such as TensorFlow, PyTorch, MLflow, scikit-learn, HuggingFace Transformers, etc.

Strong experience with Infrastructure-as-Code and CI/CD tools used to automate deployment to cloud platforms: Terraform, GitHub Actions, Concourse, Ansible, etc.

Excellent leadership, decision-making, and communication skills, with the ability to translate business needs into engineering outcomes.

Experience with AI product lifecycle management, including experimentation tracking, model governance, and release management.

Experience with Python is ideal; Java/Spring is a strong plus.

Education: Bachelor’s degree in computer science, Engineering, or a related technical field; Master’s degree preferred 

Are you the person we’re looking for?  

Provide engineering leadership and mentorship to a team of software engineers.

Be a strong engineering leader responsible for executing the Conga AI roadmap using agile practice.

Experience implementing scalable data and AI/ML solutions in a product-driven technology environment.

Manage, lead, and motivate cross-functional, interdisciplinary teams of software engineers to achieve strategic and tactical business outcomes.

Mentor and grow engineers in your team, fostering a culture of teamwork, accountability, and continuous improvement.

Demonstrated ability to skillfully hire, coach, and performance manage a team of technology professionals.

Partner with others at Conga to ensure engineering tasks are understood, broken down, and implemented to the highest of quality standards.

Collaborate with members of the team to solve challenging engineering tasks on time and with high quality.

Encourage skill growth of your team members.

Assist and adhere to enforcement of project deadlines and schedules.

Actively communicate with team members to clarify requirements and overcome obstacles to meet the team goals.

Highly technical, confident at the operational management level and comfortable in an engineering-heavy organization.

Track record of being a strong leader, with the ability to influence across teams and levels.

Work with development teams in an agile context as it relates to software development.

Here’s what will give you an edge…

Provide architectural guidance and technical oversight for AI/ML systems, ensuring solutions are scalable, secure, and production ready.

Drive engineering excellence by championing robust design, peer reviews, and efficient CI/CD practices tailored for AI systems.

Foster strong cross-functional partnerships with Product, Data Science, ML-Ops, and DevOps teams to accelerate delivery and innovation.

Travel Requirement

Limited travel to other company offices upon request

#LI-UB1

About the Role

We are looking for an experienced Staff Software Engineers to join the Veeam Data Cloud (VDC) engineering team. You will be working with a global team to build next-generation data protection services focused on ensuring data resilience for our customers. This is a unique opportunity to work on greenfield projects, building features from the ground up that will be deployed and utilized worldwide

What You'll Do

• Defines and drives technical vision for multiple areas within their team. Leads cross-team deliverables. 
• Design and develop highly reliable and scalable SaaS microservices, integrated with a variety of cloud-based managed services utilizing Golang 
• Participate in technical design discussions, reviewing code, and providing feedback to other engineers. Solves hardest, most ambiguous team wide challenges. Develops frameworks for others to follow.  
• Drives team deliverables in a timely and predictable fashion. Sets execution standards across the team. Ensure your code is high-quality, thoroughly tested, and secure 
• Facilitates effective collaboration and organizes across the engineering organization, drives discussions toward a resolution or action. Helps others across organization understand their team’s technology. 
• Participate in on-call rotation for product operations. 

What You'll Bring

• 10+ years of experience in the software development industry
• Demonstrated expertise designing, building, and implementing SaaS or large-scale cloud services on Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP)
• Expertise with a modern programming language like Go, C#, Java, Scala, or Rust
• Experience with Docker, Kubernetes or other container and orchestration tools
• Experience designing and implementing event-driven architectures
• Expertise in developing, testing, and debugging production-quality, scalable, concurrent systems
• A strong knowledge of computer science fundamentals (data structures and algorithms)
• Passion for code quality, extensibility, coding standards, testing, and automation

Bonus Skills

• Experience in the data protection industry

What You'll Get

• 18 paid vacation days, plus 4 extra global VeeaMe Days for self-care and 24 paid volunteer hours annually through Veeam Cares
• Private medical coverage for you and up to four dependents
• Life, accident, and disability insurance with enhanced coverage
• Annual flexible wellbeing allowance for physical and mental wellness
• Free confidential counselling and coaching via Employee Assistance Program (EAP), including legal and financial advice
• Meal, fuel, and transportation benefits based on work arrangement
• Daycare reimbursement and safe cab facility for eligible employees
• Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O’Reilly), mentoring, workshops, and learning events like our annual Global Day of Learning

Please note: If the applicant is permanently located outside India, Veeam reserves the right to decline the application.

About the Role

We are looking for a Senior Staff Software Engineer to join the Veeam Data Cloud (VDC) engineering team at its highest individual contributor tier. In this role, you will shape the long-term technical direction of VDC, operating with organization-wide scope and influence. You will define architectural standards, resolve the most complex and ambiguous engineering challenges, and mentor a generation of Staff and senior engineers.

This is a rare opportunity to be a founding technical voice on greenfield projects — designing next-generation data protection services that will be deployed and relied upon worldwide. You will be a trusted partner to engineering leadership, product management, and cross-functional stakeholders, bridging technical excellence with business strategy.

What You’ll Do

• Technical Strategy & Architecture

  • Own and drive the multi-year technical roadmap for the Veeam Data Cloud platform, aligning engineering decisions with company strategy.
  • Define, communicate, and enforce architectural standards, design patterns, and engineering principles across multiple teams and product areas.
  • Lead architectural reviews for major systems, proactively identifying structural risks and establishing long-term scalability and resilience frameworks.
  • Author foundational technical design documents (RFCs, ADRs) that serve as the engineering blueprint for the broader organization.

  Engineering Execution & Excellence

  • Personally contribute to the most critical, highest-complexity engineering challenges across the organization — including those that span team boundaries.
  • Design and develop highly reliable, planet-scale SaaS microservices integrated with managed cloud services (Azure, AWS, GCP) using Golang and modern cloud-native patterns.
  • Establish and champion engineering quality standards: testing philosophy, observability, performance benchmarks, security posture, and release reliability.
  • Drive the adoption of emerging engineering practices — including AI-powered developer tooling, automated code generation, and LLM-assisted testing and debugging.

  Organizational Leadership & Influence

  • Serve as the primary technical escalation point across engineering — resolving the most ambiguous, cross-cutting problems that no individual team can own alone.
  • Partner with engineering managers and directors to set execution standards, unblock delivery, and ensure timely, predictable outcomes across multiple teams.
  • Represent engineering in cross-functional leadership forums (product, security, infrastructure, finance) and act as a decision-maker on technical trade-offs with business impact.
  • Drive the organization-wide engineering culture: nurturing psychological safety, technical curiosity, and a bias toward measurable outcomes.

  Mentorship & Talent Development

  • Provide deep technical mentorship to Staff, Senior, and mid-level engineers, accelerating their growth and expanding team capability.
  • Lead structured technical coaching, design review sessions, and architectural war-games to sharpen engineering judgment across the organization.
  • Identify, recruit, and help retain top engineering talent; actively shape the interview process and hiring bar for senior roles.

  Operational Ownership

  • Participate in on-call rotation and lead incident response for critical production events, driving root cause analysis and systematic resolution.
  • Own post-incident reviews and embed learnings into platform improvements and operational runbooks.

What You’ll Bring

• 15+ years of progressive software engineering experience, with a proven track record as a principal-level individual contributor.
• Demonstrated expertise designing, building, and operating SaaS or large-scale cloud services on Azure, AWS, or GCP at organization-defining scale.
• Deep expertise in Go (Golang) and/or other systems-grade modern languages (C#, Java, Rust, Scala); polyglot mindset with language-agnostic design thinking.
• Mastery of distributed systems: event-driven architecture, eventual consistency, high-availability design, and fault-tolerant microservices.
• Expert-level command of container orchestration (Kubernetes, Docker) and cloud-native infrastructure patterns.
• Exceptional communication and leadership skills — able to drive clarity and alignment across engineering, product, and executive audiences.
• Strong computer science fundamentals: data structures, algorithms, concurrency, and systems design.
• A track record of delivering greenfield projects from zero to production at scale, with full ownership of quality, security, and reliability.

Bonus Skills

• Experience leveraging AI capabilities (LLMs, generative AI, co-pilot tooling) in product development workflows — either as a feature builder or as a force-multiplier for engineering productivity. 
• Hands-on experience building AI-powered features into SaaS products: integrating model APIs, designing prompt pipelines, or building AI-assisted developer experiences.
• Background in data protection, backup, disaster recovery, or storage infrastructure.
• Experience in platform or infrastructure engineering serving other engineering teams as internal customers.

What You’ll Get

• 18 paid vacation days, plus 4 extra global VeeaMe Days for self-care and 24 paid volunteer hours annually through Veeam Cares
• Private medical coverage for you and up to four dependents
• Life, accident, and disability insurance with enhanced coverage
• Annual flexible wellbeing allowance for physical and mental wellness
• Free confidential counselling and coaching via Employee Assistance Program (EAP), including legal and financial advice
• Meal, fuel, and transportation benefits based on work arrangement
• Daycare reimbursement and safe cab facility for eligible employees
• Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O’Reilly), mentoring, workshops, and learning events like our annual Global Day of Learning

Please note: If the applicant is permanently located outside India, Veeam reserves the right to decline the application. 

#LI-KP1

Title: Staff Site Reliability Engineer, Product Area Focus
Location: Noida / Bangalore (Hybrid)

Summary of role

Own availability, the most important product feature, by continually striving for sustained operational excellence of Sumo’s planet-scale observability and security products.  Work alongside your global SRE team, executing on projects in your product-area specific reliability roadmap, to optimize operations, increase efficiency in our use of cloud resources and our developer’s time, harden security posture, and increase feature velocity of our developers  Work closely with multiple teams to optimize the operations of their microservices - and improve the lives of the engineers within your product area engineering team.

Responsibilities

• Support the engineering teams within your product area by maintaining and executing a reliability roadmap of opportunities for improvement for reliability, maintainability, security, efficiency, and velocity - and help for realizing those opportunities.
• Collaborate with development infrastructure, Global SRE, and your product area engineering teams to establish and continually refine your reliability roadmap.
• Participate in defining, evolving, and managing SLOs for several teams within your product area.
• Participate in on-call rotations within your product area to understand operations workload so you can continually work to improve the on-call experience and reduce operational workload for running microservices and related components.
• Complete projects to optimize and tune on-call experience for your engineering teams.
• Continually improve the lifecycle of microservices and architectural components from inception and design, through deployment, operation, and refinement.
• Write code and automation to reduce operational workload, increase efficiency, improve security posture, eliminate toil, and enable Sumo’s developers to deliver features more rapidly.
• Work closely with the developer infrastructure teams to expedite development infrastructure adoption of tools to advance your reliability roadmap by identifying needs for your supported engineering teams, and contributing back features and bug fixes when needed. 
• Scale systems sustainably through mechanisms like automation, and evolve systems by pushing for changes that improve reliability and velocity.
• Facilitate blame-free root cause analysis meetings for incidents to learn and drive improvement
• Participate in and continually improve our global IRC (incident response coordination) for all products.
• Drive root cause identification and issue resolution with the teams.
• Work inside of a fast-paced iterative environment.
• Hire & Mentor new team members.

Required Qualifications and Skills

• Cloud native application development experience leveraging best practices and design patterns
• Strong debugging and trouble-shooting skills across the entire technology stack
• Deep understanding of AWS Networking, Compute, Storage, and managed services.
• Competency with modern CI/CD tooling like Kubernetes, Terraform, Ansible & Jenkins
• Experience with full life cycle support of services, from creation to production support
• Versed in Infrastructure as Code practices using technologies like Terraform or Cloud Formation
• Ability to author production ready code in at least one the following: Java, Scala or Go.
• Experience with Linux systems and at home on the command line
• Understand and apply modern approaches to cloud-native software security
• Experienced with agile frameworks, such as Scrum and Kanban, and how to operate within these frameworks to continually deliver value.
• Flexible and willing to step into new roles and responsibilities
• Willingness to learn and use Sumo Logic products for solving reliability and security issues
• Bachelor’s or Master's Degree in Computer Science, Electrical Engineering, or another scientific or technical discipline
• 8+ years of professional experience in applied software security role

Desirable Skills

• Experience using Sumo Logic products or other observability products for reliability and security
• Experienced with planet scale product development
• Running and operating SaaS products on AWS Cloud with expert level proficiency
• Experience with streaming technologies like Kafka, Kafka Streams, or KSQL
• Expert level experience in one or more of: Java, Go, Scala, or Python
• Expert level experience in one or more of: Terraform, Jenkins, Kubernetes
• Extensive experience running and tuning JVM workloads at scale

About Us

Sumo Logic, Inc. helps make the digital world secure, fast, and reliable by unifying critical security and operational data through its Intelligent Operations Platform. Built to address the increasing complexity of modern cybersecurity and cloud operations challenges, we empower digital teams to move from reaction to readiness—combining agentic AI-powered SIEM and log analytics into a single platform to detect, investigate, and resolve modern challenges. Customers around the world rely on Sumo Logic for trusted insights to protect against security threats, ensure reliability, and gain powerful insights into their digital environments. For more information, visit www.sumologic.com.

Sumo Logic Privacy Policy. Employees will be responsible for complying with applicable federal privacy laws and regulations, as well as organizational policies related to data protection.

Overview

Mesh is looking for a Staff Backend Engineer to help architect and build the foundation of the world’s first global crypto payments network. In this role, you’ll operate across the full stack — from backend systems to user-facing applications — driving key technical decisions and shaping the future of digital finance. You’ll work closely with cross-functional teams across engineering, product, and design to lead the development of secure, seamless, and scalable financial experiences. As one of our most senior technical leaders, you’ll play a critical role in defining system architecture, mentoring engineers, and influencing product strategy. We’re especially excited to meet candidates based in Bangalore, where you’ll collaborate closely with our core team and help drive our engineering culture from the ground up.

What You'll Do

• Develop and maintain backend services and APIs
• Work closely with Azure cloud services for deployment and scalability.
• Conduct thorough testing, debugging, and prompt resolution of software defects.
• Stay up-to-date with industry trends and emerging technologies.
• Contribute to code reviews, architectural decisions, and system design.
• Create high-quality, efficient, and reliable code following best practices.
• Optimize performance and scalability of backend systems for handling large data volumes.
• Stay updated on the latest trends and technologies in fintech/crypto and backend development.
• Build crypto first and secure products to facilitate traditional financial activity, such as payments, transfers etc.

Who You Are

• 7+ years of professional experience as a Backend Software Engineer, preferably in the fintech/crypto industry.
• Strong proficiency in backend programming languages such as C#, Java, Scala, Go, etc.
• A security first mindset: with good knowledge of common security concepts such as AuthN, AuthZ, SAML, API security, encryption etc
• An understanding of crypto concepts such as blockchains, RPCs, self custody wallets etc
• Solid understanding of RESTful API design principles and microservices architecture.
• Familiarity with cloud platforms such as Azure.
• Knowledge of DevOps practices and CI/CD pipelines.
• Excellent communication and collaboration abilities.

Title: Principal Data Software Engineer

Location: Bengaluru (Bangalore)

Opportunity:

Get Well is seeking a visionary and technically adept Principal Data Software Engineer to design, develop and optimize our cloud-native data platform supporting AI, analytics, and clinical applications. This role is pivotal in architecting scalable, secure, and high-performance data processing systems within a regulated healthcare environment. 

The ideal candidate brings deep technical expertise in data engineering, data governance, cloud infrastructure and hands-on experience with healthcare data standards, EMR integration, and clinical ontologie. The candidate should have a proven record of building secure, scalable data platforms. This position reports to the Senior Director, Product Development. This role requires close collaboration with data engineers, product teams, AI engineers and compliance specialists to deliver accurate, accessible, and actionable data solutions.

Responsibilities:

Platform Architecture & Design

• Design and implement scalable, secure, and efficient data architectures (e.g., data lakehouse, medallion architecture) to support enterprise AI and analytics initiatives.
• Define technical specifications for data ingestion, transformation, storage, and access layers.
• Evaluate and recommend tools, frameworks, and cloud infrastructure to optimize performance, scalability, and cost-efficiency.
• Ensure architectural alignment with organizational AI, analytics, and clinical application goals.

Data Engineering & Operations

• Develop and maintain high-performance data pipelines for both batch and streaming data processing, leveraging tools such as Apache Spark, Databricks or Snowflake.
• Implement deployment and orchestration frameworks (e.g., Airflow, dbt, Spark, Databricks, Snowflake)
• Adhere to engineering best practices for CI/CD, observability, incident response, and SLA adherence
• Optimize data processing systems for performance, reliability, and reusability.

• Mentor engineers to foster technical growth and ensure alignment with best practices in data engineering.

Governance, Quality & Compliance

• Collaborate with data governance and compliance teams to implement data security, encryption, and access control policies in line with HIPAA, GDPR, and other regulations.
• Ensure data quality through validation, lineage tracking, and metadata management practices.
• Support the development and maintenance of data catalogs for discoverability and stewardship

Collaboration & Technical Leadership

• Partner with data engineers, product teams, and clinical informatics specialists to deliver integrated data solutions.
• Provide technical guidance and mentorship to junior engineers, fostering a culture of technical excellence and collaboration.
• Communicate complex technical designs and solutions to both technical and non-technical stakeholders effectively.
• Contribute to agile development practices, participating in code reviews, design discussions, and continuous improvement initiatives.

Innovation & Technical Expertise

• Stay informed about emerging trends in data engineering, cloud-native architectures, and healthcare data standards.
• Prototype and evaluate modern data frameworks and tools (e.g., Delta Lake, Apache Iceberg) to enhance platform capabilities.
• Communicate complex technical concepts to both technical and non-technical stakeholders.
• Advocate for best practices in data platform design to ensure scalability, explainability, and compliance in AI-driven systems.

Requirements:

Education & Experience

• Bachelor’s or Master’s degree in Computer Science or related technical discipline
• 12+ years of experience with at least 4+ years in data engineering or platform roles
• Proven track record of designing and implementing cloud-native data processing systems.

Technical Proficiency

• Advanced proficiency in Python, Scala, SQL, and distributed data processing frameworks such as Apache Spark.
• Hands-on experience with cloud-based data platforms (e.g., Databricks, Snowflake, BigQuery, or Redshift).
• Demonstrated ability to transform raw clinical data into curated, ML-ready datasets for analytics and AI applications.
• Hands-on expertise in healthcare data standards including FHIR, HL7, USCDI, and CCDA

Professional Skills

• Strong problem-solving skills with a focus on delivering scalable and maintainable solutions.
• Excellent collaboration and communication skills, with the ability to work effectively in cross-functional teams.
• Proactive, detail-oriented, and committed to driving technical excellence in a fast-paced environment.

About Get Well:

Now part of the SAI Group family, Get Well is redefining digital patient engagement by putting patients in control of their personalized healthcare journeys, both inside and outside the hospital. Get Well is combining high-tech AI navigation with high-touch care experiences driving patient activation, loyalty, and outcomes while reducing the cost of care. For almost 25 years, Get Well has served more than 10 million patients per year across over 1,000 hospitals and clinical partner sites, working to use longitudinal data analytics to better serve patients and clinicians. AI innovator SAI Group led by Chairman Romesh Wadhwani is the lead growth investor in Get Well. Get Well’s award-winning solutions were recognized again in 2024 by KLAS Research and AVIA Marketplace. Learn more at Get Well and follow-us on LinkedIn and Twitter.

Get Well is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age or veteran status.

About SAI Group:

• SAIGroup commits to $1 Billion capital, an advanced AI platform that currently processes 300M+ patients, and 4000+ global employee base to solve enterprise AI and high priority healthcare problems. SAIGroup - Growing companies with advanced AI; https://www.cnbc.com/2023/12/08/75-year-old-tech-mogul-betting-1-billion-of-his-fortune-on-ai-future.html
• Bio of our Chairman Dr. Romesh Wadhwani: Team - SAIGroup (Informal at Romesh Wadhwani - Wikipedia)
• TIME Magazine recently recognized Chairman Romesh Wadhwani as one of the Top 100 AI leaders in the world - Romesh and Sunil Wadhwani: The 100 Most Influential People in AI 2023 | TIME

The Community You Will Join:

The Payments team at Airbnb is crucial to the company's operations, handling critical data related to compliance with Tax, Payments, and Legal regulations. We also manage application data for tools such as CRM, Jira, and Workday, which are essential for Airbnb’s business. Joining this team means working with cross-functional stakeholders, designing scalable solutions, and contributing to a world-class data engineering environment with an emphasis on quality, scalability, and robust engineering practices.

The Difference You Will Make:

Our team charter is on enabling Airbnb to comply with Tax, Payments,and Legal regulations so that our Hosts can continue to operate in regulated geos. Our products ingest, process, validate, and deliver large datasets to government authorities (often partnered with tax remittance) so our data must be of the highest accuracy and quality. As you build and maintain key components of the critical Compliance data ecosystem, you’ll have the opportunity to contribute to creating standards and best practices for Airbnb’s DataEngineering. Your work on solving complex business challenges at scale will be instrumental in shaping the tools, processes, and standards used by the broader data community.

A Typical Day:

• Design, build, and maintain robust and efficient data pipelines that collect, process, and store data from various sources, including user interactions, listing details, and external data feeds.
• Develop data models that enable the efficient analysis and manipulation of data for merchandising optimization. Ensure data quality, consistency, and accuracy.
• Develop high quality data assets for product use-cases by partnering with Product, AI/ML and Data Science.
• Build scalable data pipelines (SparkSQL & Scala) leveraging Airflow scheduler/executor framework
• Collaborate with cross-functional teams, including Data Scientists, Product Managers, and Software Engineers, to define data requirements, and deliver data solutions that drive merchandising and sales improvements.
• Contribute to the broader Data Engineering community at Airbnb to influence tooling and standards to improve culture and productivity
• Improve code and data quality by leveraging and contributing to internal tools to automatically detect and mitigate issues

Your Expertise:

• 6+ years of relevant industry experience
• BE/B.tech in computer science or relevant technical degree
• Hands on experience in DSA coding -Data structure and algorithm
• Extensive experience designing, building, and operating robust distributed data platforms (e.g., Spark, Kafka, Flink, HBase) and handling data at the petabyte scale.
• Strong knowledge of Scala, or Python, and expertise with data processing technologies and query authoring (SQL).
• Demonstrated ability to analyze large data sets to identify gaps and inconsistencies, provide data insights, and advance effective product solutions
• Expertise with ETL schedulers such as Apache Airflow, Luigi, Oozie, AWS Glue or similar frameworks
• Solid understanding of data warehousing concepts and hands-on experience with relational databases (e.g., PostgreSQL, MySQL) and columnar databases (e.g., Redshift, BigQuery, HBase, ClickHouse)
• Excellent written and verbal communication skills

Our Commitment To Inclusion & Belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply.

As a Senior Data Engineer, you will design, build, and manage components of our highly available real-time and batch data pipelines handling petabytes of data. Our pipeline platform is designed to ingest and process billions of events per day, and make the resulting aggregations and insights available within minutes in our analytical data stores. Data Analytics is at the core of our business, and we’re constantly innovating to make our systems more performant, timely, cost-effective, and capable while maintaining high reliability. You will build on top of our core data infrastructure and pipelines using technologies and tools tailored for massive data sets including Flink, Spark, Kafka, Iceberg and Druid while working in the AWS cloud environment.  

If you are interested in building systems that can consume and explore billions of data points a day, work with petabytes of data, and want to push what is possible with data, this is the place for you!

As a Senior Data Engineer, you’ll get to:

• Architect, build, and own real-time and batch data aggregation systems to deliver quality analytical reports for our internal and external customers.
• Collaborate with Data Scientists, Backend Engineers, Data & Infrastructure Operations, and Products Managers to deliver new features and capabilities for customers.
• Develop clean, safe, testable, and cost-efficient solutions.
• Make well-informed decisions with deep knowledge of both the internal and external impacts on teams and projects. Foresee shortcomings ahead of time and be able to drive to resolution.

You’ll be a good fit if you have:

• Bachelors in CS or equivalent.
• 6+ years of Software Engineering or Data Engineering experience with a recent focus on big data. 
• Strong development skills in Java or Scala and Python.
• Solid background in the fundamentals of computer science, distributed systems, large scale data processing as well as database schema design and data warehousing.
• Practical experience managing AWS or Google Cloud environments.
• Experience in containerized deployment or Kubernetes is a big plus!
• Good understanding of a broad spectrum of NoSQL, traditional RDBMS, and analytical/columnar data stores including Postgres, Druid, Vertica, Redshift, Hadoop, Hive, Cassandra, Aerospike, and Redis.
• Ability to build systems that balance scalability, availability, and latency.
• Strong ability to advocate for the continual deployment and automation tools, monitoring, and self-healing systems that can help improve the lives of our engineers.
• Great communication skills and you are a team player who has a proven track record of building strong relationships with management, co-workers, and customers.
• A desire to learn and grow, push yourself and your team, share lessons with others and provide constructive and continuous feedback, and be receptive to feedback from.

This role will be based at our Bengaluru, KA office and follows a Hybrid schedule that will be aligned with our Return to Office guidelines. 

The Community You Will Join:

The BizTech team at Airbnb is crucial to the company's operations, handling critical data related to compliance with Tax, Payments, and Legal regulations. We also manage application data for tools such as CRM, Jira, and Workday, which are essential for Airbnb’s business. Joining this team means working with cross-functional stakeholders, designing scalable solutions, and contributing to a world-class data engineering environment with an emphasis on quality, scalability, and robust engineering practices.

The Difference You Will Make:

We are looking for a hands-on expert to provide technical leadership in addressing BizTech’s diverse data engineering needs and driving long-term strategies and best practices. This key leadership role requires strong collaboration and influence across teams. You'll play a crucial role in understanding business needs, identifying the right data sources, designing efficient data models, and building reliable, scalable data pipelines. As technology continues to evolve, you'll help shape and maintain significant parts of BizTech’s critical data ecosystem. Your contributions will not only address complex business challenges but also help refine and advance Airbnb’s Data Engineering Paved Path, benefiting the entire data community at Airbnb. We believe in solving problems and contributing back to our data community to continuously improve.

A Typical Day /Responsibilities:

• Lead the design, implementation, and testing of data systems, from architecture to production.
• Build batch and real-time data systems that support business needs and critical products.
• Ensure data systems' quality, performance, and stability through rigorous monitoring and quality assurance practices.
• Design and optimize data models to efficiently meet business and product requirements.
• Collaborate with cross-functional teams, including product managers, data scientists, and engineers, to develop scalable systems and drive data-driven decisions.
• Maintain strong partnerships with backend, data science, and machine learning teams to ensure seamless integration of data systems.
• Contribute to long-term data strategies and influence data engineering practices across the organization.
• Mentor and guide team members, fostering best practices for data quality and governance.
• Advance 3rd party data integrations, enhancing frameworks for data exchange, governance, and lineage.

Your Expertise:

• 9+ years of relevant experience with a Bachelor's/Master’s degree in CS/EE (or 6+ years with a PhD).
• Extensive experience in designing, building, and operating distributed data platforms (e.g., Spark, Kafka, Flink) at a large scale.
• Proficiency in Java, Scala, or Python, along with strong skills in data processing and SQL querying.
• Proven track record of designing and optimizing batch and real-time data pipelines.
• Strong collaboration skills with the ability to work with product managers, data scientists, and engineers.
• Advanced problem-solving and analytical skills, with a focus on data quality, governance, and system reliability.
• Excellent written and verbal communication, with the ability to influence stakeholders and convey complex technical concepts.
• Expertise in data modeling, warehousing, and working with relational and columnar databases (e.g., PostgreSQL, MySQL, Redshift, BigQuery).
• Experience with integrating machine learning models into data systems (preferred).
• Strong leadership and mentorship capabilities, with experience guiding teams on best practices and technical strategies.
• Flexible and innovative, with the ability to adopt new technologies to enhance data systems and processes.

Build and operate the production backbone that takes models from Applied Sciences (AS) and delivers reliable, low-latency ML services across Tekion’s DMS, CRM, Digital Retail, Service, Payments, and enterprise products. You’ll own pipelines, microservices, CI/CD, observability, and runtime reliability—working hand-in-hand with Applied Sciences and Product to turn ideas into measurable dealer and consumer impact. 

Why this Role Matters 

• Accelerate the rollout of LLM-powered and agent-driven features across Tekion products. 

• Enable agentic workflows that automate, reason, and interact on behalf of users and internal stakeholders. 

• Operationalize secure, compliant, and explainable LLM and agentic services at scale. 

• Convert Applied Sciences models into scalable, compliant, cost‑efficient production services. 

• Standardize how models are trained, validated, deployed, and monitored across Tekion products. 

• Power real-time, context-aware experiences by integrating batch/stream features, graph context, and online inference. 

What You’ll Do 

• Turn Applied Sciences prototype models (tabular, NLP/LLM, recommendation, forecasting) into fast, reliable services with well-defined API contracts. 

• Integrate with the LLM Gateway/MCP, prompt/config versioning. 

• Build and orchestrate CI/CD pipelines. 

• Review data science models; refactor and optimize code; containerize; deploy; version; and monitor for quality. 

• Collaborate with data scientists, data engineers, product managers, and architects to design enterprise systems. 

• Monitor, detect, and mitigate risks unique to LLMs and agentic systems. 

• Implement prompt management: versioning, A/B testing, guardrails, and dynamic orchestration based on feedback and metrics. 

• Design batch/stream pipelines (Airflow/Kubeflow, Spark/Flink, Kafka) and online features linked to our domain graph. 

• Build inference microservices (REST/gRPC) with schema versioning, structured outputs, and stringent p95 latency targets. 

• Manage the model/feature lifecycle: feature store strategy, model/agent registry, versioning, and lineage. 

• Instrument deep observability: traces/logs/metrics, data/feature drift, model performance, safety signals, and cost tracking. 

• Ensure real-time reliability: autoscaling, caching, circuit breakers, retries/fallbacks, and graceful degradation. 

• Develop templates/SDKs/CLIs, sandbox datasets, and documentation that make shipping ML the default path. 

Desired Skills and Experience 

• 6+ years in ML engineering/MLOps or backend/platform engineering with production ML. 

• Experience with LLMs, retrieval systems, vector stores, and graph/knowledge stores. 

• Strong software engineering fundamentals: Python plus one of Java/Go/Scala; API design; concurrency; testing. 

• Hands-on with orchestration frameworks and libraries (LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, etc.). 

• Knowledge of agent architectures (reactive, planning, retrieval-augmented agents), and safe execution patterns. 

• Pipelines and data: Airflow/Kubeflow or similar; Spark/Flink; Kafka/Kinesis; strong data quality practices. 

• Microservices and runtime: Docker/Kubernetes, service meshes, REST/gRPC; performance and reliability engineering. 

• Model ops: experiment tracking, registries (e.g., MLflow), feature stores, A/B and shadow testing, drift detection. 

• Observability: OpenTelemetry/Prometheus/Grafana; debugging latency, tail behavior, and memory/CPU hotspots. 

• Cloud: AWS preferred (IAM, ECS/EKS, S3, RDS/DynamoDB, Step Functions/Lambda), with cost optimization experience. 

• Security/compliance: secrets management, RBAC/ABAC, PII handling, auditability. 

Preferred Mindset 

• Product-oriented: You measure success by dealer and consumer outcomes, not just technical metrics. 
• Reliability- and safety-first: You move fast with guardrails, rollbacks, and clear SLOs. 
• Systems thinker: You design for multi-tenant scale, portability, and cost efficiency
• Collaborative: You translate between Applied Sciences, Product, and the Data & AI Platform; you document and teach. 

Senior Software Engineer  -  Senior Machine Learning Platform

Why This Role Matters 

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.  

What Makes This Opportunity Unique 

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue.  You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models . 

Responsibilities 

• Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking. 

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics). 

• Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction. 

• Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning. 

• Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval. 

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows. 

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging. 

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance. 

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems. 

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines. 

• Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage. 

• Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost. 

• Run continuous offline/online evaluations for quality, factuality, bias, and safety for the platform sanity. 

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls. 

• Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support compliances where needed. 

• Provide templates/CLIs, sandboxes, and docs so product teams can build and ship fast; mentor engineers and champion MLOps and AI safety best practices. 

Desired Skills & Experience 

• 5 - 7 years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems). 

• Production experience with Python plus one of Java/Scala/Go; microservices and API design. 

• MLOps at scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka). 

• Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS. 

• Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows. 

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting. 

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry. 

• Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph), GraphQL, vector search (e.g., pgvector/Qdrant/Milvus), hybrid retrieval patterns. 

Preferred Mindset 

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs. 

• Thinks in systems - observability, fallback, access control are core, not afterthoughts. 

• Passionate about AI - enjoys enabling real-world LLM and agentic use cases. 

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation. 

• Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience. 

• Documentation and teaching: you make complex systems understandable; you uplevel teams. 

Why project44?  

At project44, we believe in better.  

We challenge the status quo because we know a better supply chain isn’t just possible—it’s essential. Better for our customers. Better for their business. Better for the world.  

With our Decision Intelligence Platform, Movement, we’re redefining how global supply chains operate. By transforming fragmented logistics data into real-time, AI-powered insights, we empower companies to connect instantly, see clearly, act decisively, and automate intelligently. Our Supply Chain AI enhances visibility, drives smarter execution, and unlocks next-gen applications that keep businesses moving forward.  

Headquartered in Chicago, IL with a 2nd HQ in Bengaluru, India we are powered by a diverse global team that is tackling the toughest logistics challenges with innovation, urgency, and purpose.  

If you’re driven to solve meaningful problems, leverage AI to scale rapidly, drive impact daily, and be part of a high-performance team – we should talk. 

Description:    

project44 is looking for a Staff Data Engineer to join our engineering team. You will work in a fast-paced Agile environment designing, building, and implementing best-in-class integrations to accelerate how project44 connects to the world’s logistics networks.   

About the Role

We are looking for a Staff Data Engineer to drive the execution and scaling of project44’s data platform.This role sits at the intersection of Data Engineering, Data Science, and Product, with a mandate to translate architectural vision into reliable, scalable systems used across teams.

You will partner closely with other data engineers to implement platform direction, establish engineering standards, and ensure consistent, high-quality execution across data systems. Your focus will be on solving complex data challenges, driving adoption of best practices, and enabling teams to build efficiently on a shared foundation.

As AI reshapes how data systems operate, you will also help integrate agentic and GenAI-powered workflows into data pipelines and developer workflows—improving automation, observability, and system intelligence.

What You’ll Do

Drive Platform Execution

• Translate architectural direction into production-grade systems across ingestion, transformation, and serving layers.

Establish Standards & Best Practices

• Define and enforce patterns for data modeling, pipeline design, observability, testing, and data quality across teams.

Solve Complex Data Problems

• Own and resolve high-impact technical challenges related to scale, latency, reliability, and cost efficiency.

Enable Consistency Across Teams

• Work across multiple teams to drive adoption of shared data models, tooling, and engineering practices.

Build Scalable Data Systems

• Develop and optimize pipelines and data systems for performance, maintainability, and long-term scalability.

Integrate AI-Native Workflows

• Implement agentic and GenAI-driven workflows to automate data operations, improve monitoring, and accelerate development.

Mentor & Raise the Bar

• Act as a technical leader within the team, mentoring junior engineers and elevating engineering quality across the organization.

What We’re Looking For

8–10+ years in Data Engineering or Software Engineering, with a strong track record of building and scaling production-grade systems.

Technical Expertise

• Strong programming skills (Python preferred, Java/Scala a plus) and deep experience with modern data technologies (Spark, Snowflake, Databricks).

Data Systems & Execution

• Proven ability to build and operate reliable, scalable data pipelines and systems in production environments.

Data Modeling & Warehousing

• Strong experience in data modeling, warehousing, and building analytics- and ML-ready datasets.

Distributed Systems & Trade-offs

• Ability to navigate trade-offs across performance, cost, reliability, and scalability in cloud-native systems.

AI & Modern Tooling

• Experience leveraging GenAI or agentic tools to improve developer productivity, automate workflows, or enhance data systems.

Cross-Team Influence

• Experience working across teams to drive alignment, adoption of standards, and consistent execution.

Communication & Leadership

• Strong communication skills and the ability to influence technical decisions across teams.

Preferred Skills

• Experience with real-time and event-driven systems (Kafka, streaming architectures)
• Familiarity with orchestration tools (Airflow, Argo) and containerization (Docker, Kubernetes)
• Experience with data governance and access control (RBAC)
• Exposure to analytics and BI tools (Looker, Tableau)
• Experience building shared data platforms or internal tooling
• Background in logistics, supply chain, or high-scale operational systems

What Success Looks Like (6–12 Months)

• Successfully implemented core data platform patterns across multiple teams
• Improved data reliability, observability, and quality in critical pipelines
• Reduced inconsistency in data models and engineering practices across teams
• Enabled faster development and deployment of data and ML workflows
• Introduced AI-driven improvements in data workflows and developer productivity
• Acted as a key technical leader, raising the bar for execution and engineering quality

In-office Commitment: This position requires a commitment to contribute to our collaborative culture by working in-office three days weekly. 

Diversity & Inclusion 

At project44, we're designing the future of how the world moves and is connected through trade and global supply chains. As we work to deliver a truly world-class product and experience, we are also intentionally building teams that reflect the unique communities we serve. We’re focused on creating a company where all team members can bring their authentic selves to work every day. 

We’re building a company that every one of us at project44 is proud to work for, and our journey of becoming a more diverse, equitable and inclusive organization, where all have a sense of belonging, is shaped through the actions of our leadership, global teams, and individual team members. We are resolute in our belief that each team member has an equal responsibility to mold and uphold our culture. 

project44 is an equal opportunity employer seeking to enrich our work environment by creating opportunities for individuals of all backgrounds and experiences to thrive. If you share our values and our passion for helping the way the world moves, we’d love to review your application! 

For any accommodation needed during the hiring process, please email   recruiting@project44.com. Even if you don’t meet 100% of the above job description you should still seriously consider applying. Studies show that you can still be considered for a role if you meet just 50% of the role’s requirements. 

More about project44 

Since 2014, project44 has been transforming the way one of the largest, most important global industries does business. As transportation and logistics continue to evolve and customer expectations around delivery become more demanding, industry technology must rise to the occasion. In just a few short years, we have created a digital infrastructure that eliminates the inefficiencies caused by dated technology and manual processes. Our Advanced Visibility Platform is used by the world’s leading brands to track shipments, collaborate with supply chain partners, drive operational efficiencies, and create outstanding customer experiences. 

Why project44?  

At project44, we believe in better.  

We challenge the status quo because we know a better supply chain isn’t just possible—it’s essential. Better for our customers. Better for their business. Better for the world.  

With our Decision Intelligence Platform, Movement, we’re redefining how global supply chains operate. By transforming fragmented logistics data into real-time, AI-powered insights, we empower companies to connect instantly, see clearly, act decisively, and automate intelligently. Our Supply Chain AI enhances visibility, drives smarter execution, and unlocks next-gen applications that keep businesses moving forward.  

Headquartered in Chicago, IL with a 2nd HQ in Bengaluru, India we are powered by a diverse global team that is tackling the toughest logistics challenges with innovation, urgency, and purpose.  

If you’re driven to solve meaningful problems, leverage AI to scale rapidly, drive impact daily, and be part of a high-performance team – we should talk. 

Description: 

Project44 is looking for a Data Engineer to join our engineering team. You will work in a fast-paced Agile environment designing, building, and implementing scalable micro-service systems consumed by companies around the world. 

What you’re most excited for: 

• Designing and writing ETL jobs, data monitoring, and data cleaning.  
• Building optimized data pipelines.
• Enjoy database related work: Snowflake/Postgres/MongoDB.  

• Applying software rigor to create reliable, easily extensible data systems. 
• Collaborate with product on prioritization and scheduling of product and engineering features 
• Be a positive role model. 

Requirements: 

• B.Tech/BE in Computer Science, Engineering or related field or above.  
• 3+ years of experience in SQL, schema design and data analytics.  
• Hands on in Programming proficiency in any programming language like Python(preferred), Java, Scala. 
• Experience with data system orchestration and designing pipelines 
• Experience with Agile development methodologies and supporting tools such as Jira, Confluence, Rally, etc. 
• Dynamic & driven individual with enthusiasm for working in a high-profile, fast-paced entrepreneurial environment  
• Proven track record of working in a fast paced, competing priority environment, capable of partnering with and communicating across multiple functions and teams 
• Excellent communications skills and can speak directly to executives and customers in fast-developing situations, and write critical customer communications 

Preferred Experience: 

• Experience in database administration, including Role Based Access Control (RBAC) 
• Experience with data visualization tools, e.g., Looker or Tableau 
• Understand tradeoffs and optimize towards different metrics such as cost, time, memory, or compute. 
• Develop collaborative relationships with diverse internal teams, like Sales, Infosec, Business Intelligence, or other product teams 
• Familiarity with Python, DBT, Kafka, Argo, Google Cloud, Kubernetes, Docker, and Snowflake 
• Knowledge of Real time Analytics / event driven systems 

Diversity & Inclusion  

At project44, we're designing the future of how the world moves and is connected through trade and global supply chains. As we work to deliver a truly world-class product and experience, we are also intentionally building teams that reflect the unique communities we serve. We’re focused on creating a company where all team members can bring their authentic selves to work every day.  

We’re building a company that every one of us at project44 is proud to work for, and our journey of becoming a more diverse, equitable and inclusive organization, where all have a sense of belonging, is shaped through the actions of our leadership, global teams and individual team members. We are resolute in our belief that each team member has an equal responsibility to mold and uphold our culture.  

project44 is an equal opportunity employer seeking to enrich our work environment by creating opportunities for individuals of all backgrounds and experiences to thrive. If you share our values and our passion for helping the way the world moves, we’d love to review your application!  

For any accommodation needed during the hiring process, please email recruiting@project44.com. Even if you don’t meet 100% of the above job description you should still seriously consider applying. Studies show that you can still be considered for a role if you meet just 50% of the role’s requirements.  

About project44  

Since 2014, project44 has been transforming the way one of the largest, most important global industries does business. As transportation and logistics continues to evolve and customer expectations around delivery become more demanding, industry technology must rise to the occasion. In just a few short years, we’ve created a digital infrastructure that eliminates the inefficiencies caused by dated technology and manual processes. Our Advanced Visibility Platform is used by the world’s leading brands to track shipments, collaborate with supply chain partners, drive operational efficiencies, and create outstanding customer experiences.   

A candidate will be asked to conduct a pre-employment criminal background screening test as a standard routine to minimize moral & integrity risks among our staff and to ensure Project44 is compliant with its internal pre-employment screening policy and SOC 2, Sarbanes Oxley Act and ISO 27001 compliance. We will only ask you to conduct a criminal background check when we believe it is proportionate to the role that you will fulfil within your team and/or department   A criminal  back ground check will be facilitated by our third party vendor Accurate who will assist you to get the appropriate documents from authorities in the country where you will be employed.  Depending on the country where you will be employed your local authority will provide a Certificate of Conduct or similar document upon your request.  If there are any costs related to screening, these will be borne by Project44. When our vendor Accurate will reach out to, you will be asked for your consent. We would like to emphasize  that at all time you can decide  to withdraw from the application process and/or to withdraw the consent to perform a screening test. 

#LI-Hybrid 

Why This Role Matters
We're looking for a Data Platform Engineer with deep systems engineering expertise and a passion for building reusable infrastructure. You'll help
architect and deliver the internal frameworks that power data ingestion, transformation, observability, and self-serve analytics across the company. Your work will be central to enabling scalable, multi-tenant ingestion pipelines (including CDC via Debezium), serving layers built on Trino, and robust tooling that lets teams move fast without compromising on data quality or platform
health.

What Makes This Opportunity Unique
Build platform tooling for multi-region, multi-tenant data infrastructure using modern open-source and cloud-native technologies.
Lead engineering efforts around CDC pipelines using Debezium, enabling reliable change capture from application databases.
Shape the developer experience for data engineers, from ingestion to publishing and consumption, with metadata-aware automation.
Drive the evolution of data serving infrastructure using Trino and open table formats like Delta Lake or Iceberg.
Work at the intersection of data engineering, infrastructure, and platform strategy, directly influencing core architectural decisions.

Responsibilities

• Design and implement foundational frameworks for ingestion, orchestration, schema validation, and metadata management.
• Build robust, scalable pipelines for Change Data Capture (CDC) using Debezium integrated with Kafka and Spark.
• Develop internal tooling to standardize and accelerate data ingestion, transformation, and publishing workflows.
• Optimize data serving layers powered by Trino, including metadata syncing, security filtering, and performance tuning.
• Partner with SRE and Infra teams to build autoscaling, self-healing, and cost-optimized Spark jobs on AWS EMR.
• Implement observability features — logs, metrics, alerts — for critical platform services and data pipelines.
• Define and enforce standards for schema evolution, lineage tracking, and data governance.
• Automate platform operations using CI/CD pipelines, metadata-driven configurations, and infrastructure-as-code.

Desired Skills & Experience

• 7+–12 years of experience in data platform, infrastructure, or backend systems engineering roles.
• Strong systems engineering fundamentals — distributed systems, fault tolerance, performance tuning.
• Expertise in Apache Spark with production experience on AWS EMR.
• Experience building or managing CDC pipelines using tools like Debezium, Kafka Connect, or custom connectors. Familiarity with streaming systems such as Kafka, Kinesis, or Flink.
• Experience serving data using Trino or Presto in a multi-tenant environment.
• Proficiency in Python, Java or Scala, and Unix/Linux systems.
• Strong understanding of open table formats like Delta Lake or Apache Iceberg.
• Exposure to infrastructure as code (Terraform, CloudFormation) and CI/CD automation.

Preferred Mindset

• Platform-first thinker — designs systems for scale, abstraction, and reusability.
• Builder's bias — values automation, clean interfaces, and measurable SLAs.Deep collaborator — partners well across Data, Infra, and Engineering teams.
• Operational discipline — understands the impact of monitoring, logging,and reliability.

Why This Role Matters
We're looking for a Data Platform Engineer with deep systems engineering expertise and a passion for building reusable infrastructure. You'll help
architect and deliver the internal frameworks that power data ingestion, transformation, observability, and self-serve analytics across the company. Your work will be central to enabling scalable, multi-tenant ingestion pipelines (including CDC via Debezium), serving layers built on Trino, and robust tooling that lets teams move fast without compromising on data quality or platform
health.

What Makes This Opportunity Unique
Build platform tooling for multi-region, multi-tenant data infrastructure using modern open-source and cloud-native technologies.
Lead engineering efforts around CDC pipelines using Debezium, enabling reliable change capture from application databases.
Shape the developer experience for data engineers, from ingestion to publishing and consumption, with metadata-aware automation.
Drive the evolution of data serving infrastructure using Trino and open table formats like Delta Lake or Iceberg.
Work at the intersection of data engineering, infrastructure, and platform strategy, directly influencing core architectural decisions.

Responsibilities

• Design and implement foundational frameworks for ingestion, orchestration, schema validation, and metadata management.
• Build robust, scalable pipelines for Change Data Capture (CDC) using Debezium integrated with Kafka and Spark.
• Optimize data serving layers powered by Trino, including metadata syncing, security filtering, and performance tuning.
• Partner with SRE and Infra teams to build autoscaling, self-healing, and cost-optimized Spark jobs on AWS EMR.
• Implement observability features — logs, metrics, alerts — for critical platform services and data pipelines.
• Define and enforce standards for schema evolution, lineage tracking, and data governance.
• Automate platform operations using CI/CD pipelines, metadata-driven configurations, and infrastructure-as-code.

Desired Skills & Experience

• 5-7 years of experience in data platform, infrastructure, or backend systems engineering roles.
• Strong systems engineering fundamentals — distributed systems, fault tolerance, performance tuning.
• Expertise in Apache Spark with production experience on AWS EMR.
• Experience building or managing CDC pipelines using tools like Debezium, Kafka Connect, or custom connectors. Familiarity with streaming systems such as Kafka, Kinesis, or Flink.
• Experience serving data using Trino or Presto in a multi-tenant environment.
• Proficiency in Python, Java or Scala, and Unix/Linux systems.
• Strong understanding of open table formats like Delta Lake or Apache Iceberg.
• Exposure to infrastructure as code (Terraform, CloudFormation) and CI/CD automation.

Why This Role Matters 

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.  

What Makes This Opportunity Unique 

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue.  You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models . 

Responsibilities 

• Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking. 

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics). 

• Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction. 

• Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning. 

• Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval. 

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows. 

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging. 

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance. 

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems. 

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines. 

• Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage. 

• Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost. 

• Run continuous offline/online evaluations for quality, factuality, bias, and safety for the platform sanity. 

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls. 

• Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support compliances where needed. 

Desired Skills & Experience 

• 4 - 5+ years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems). 

• Production experience with Python plus one of Java/Scala/Go; microservices and API design. 

• MLOps at scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka). 

• Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS. 

• Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows. 

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting. 

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry. 

• Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph), GraphQL, vector search (e.g., pgvector/Qdrant/Milvus), hybrid retrieval patterns. 

Preferred Mindset 

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs. 

• Thinks in systems - observability, fallback, access control are core, not afterthoughts. 

• Passionate about AI - enjoys enabling real-world LLM and agentic use cases. 

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation. 

• Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience. 

Why This Role Matters 

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.  

What Makes This Opportunity Unique 

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue.  You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models . 

Responsibilities 

• Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking. 

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics). 

• Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction. 

• Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning. 

• Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval. 

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows. 

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging. 

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance. 

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems. 

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines. 

• Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage. 

• Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost. 

• Run continuous offline/online evaluations for quality, factuality, bias, and safety for the platform sanity. 

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls. 

• Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support compliances where needed. 

• Provide templates/CLIs, sandboxes, and docs so product teams can build and ship fast; mentor engineers and champion MLOps and AI safety best practices. 

Desired Skills & Experience 

• 5+ - 8+ years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems). 

• Production experience with Python plus one of Java/Scala/Go; microservices and API design. 

• MLOps at scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka). 

• Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS. 

• Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows. 

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting. 

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry. 

• Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph), GraphQL, vector search (e.g., pgvector/Qdrant/Milvus), hybrid retrieval patterns. 

Preferred Mindset 

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs. 

• Thinks in systems - observability, fallback, access control are core, not afterthoughts. 

• Passionate about AI - enjoys enabling real-world LLM and agentic use cases. 

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation. 

• Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience. 

Build and operate the production backbone that takes models from Applied Sciences (AS) and delivers reliable, low-latency ML services across Tekion’s DMS, CRM, Digital Retail, Service, Payments, and enterprise products. You’ll own pipelines, microservices, CI/CD, observability, and runtime reliability—working hand-in-hand with Applied Sciences and Product to turn ideas into measurable dealer and consumer impact. 

Why this Role Matters 

• Accelerate the rollout of LLM-powered and agent-driven features across Tekion products. 

• Enable agentic workflows that automate, reason, and interact on behalf of users and internal stakeholders. 

• Operationalize secure, compliant, and explainable LLM and agentic services at scale. 

• Convert Applied Sciences models into scalable, compliant, cost‑efficient production services. 

• Standardize how models are trained, validated, deployed, and monitored across Tekion products. 

• Power real-time, context-aware experiences by integrating batch/stream features, graph context, and online inference. 

What You’ll Do 

• Turn Applied Sciences prototype models (tabular, NLP/LLM, recommendation, forecasting) into fast, reliable services with well-defined API contracts. 

• Integrate with the LLM Gateway/MCP, prompt/config versioning. 

• Build and orchestrate CI/CD pipelines. 

• Review data science models; refactor and optimize code; containerize; deploy; version; and monitor for quality. 

• Collaborate with data scientists, data engineers, product managers, and architects to design enterprise systems. 

• Monitor, detect, and mitigate risks unique to LLMs and agentic systems. 

• Implement prompt management: versioning, A/B testing, guardrails, and dynamic orchestration based on feedback and metrics. 

• Design batch/stream pipelines (Airflow/Kubeflow, Spark/Flink, Kafka) and online features linked to our domain graph. 

• Build inference microservices (REST/gRPC) with schema versioning, structured outputs, and stringent p95 latency targets. 

• Manage the model/feature lifecycle: feature store strategy, model/agent registry, versioning, and lineage. 

• Instrument deep observability: traces/logs/metrics, data/feature drift, model performance, safety signals, and cost tracking. 

• Ensure real-time reliability: autoscaling, caching, circuit breakers, retries/fallbacks, and graceful degradation. 

• Develop templates/SDKs/CLIs, sandbox datasets, and documentation that make shipping ML the default path. 

Desired Skills and Experience 

• 8 - 10 years in ML engineering/MLOps or backend/platform engineering with production ML. 

• Experience with LLMs, retrieval systems, vector stores, and graph/knowledge stores. 

• Strong software engineering fundamentals: Python plus one of Java/Go/Scala; API design; concurrency; testing. 

• Hands-on with orchestration frameworks and libraries (LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, etc.). 

• Knowledge of agent architectures (reactive, planning, retrieval-augmented agents), and safe execution patterns. 

• Pipelines and data: Airflow/Kubeflow or similar; Spark/Flink; Kafka/Kinesis; strong data quality practices. 

• Microservices and runtime: Docker/Kubernetes, service meshes, REST/gRPC; performance and reliability engineering. 

• Model ops: experiment tracking, registries (e.g., MLflow), feature stores, A/B and shadow testing, drift detection. 

• Observability: OpenTelemetry/Prometheus/Grafana; debugging latency, tail behavior, and memory/CPU hotspots. 

• Cloud: AWS preferred (IAM, ECS/EKS, S3, RDS/DynamoDB, Step Functions/Lambda), with cost optimization experience. 

• Security/compliance: secrets management, RBAC/ABAC, PII handling, auditability. 

Preferred Mindset 

• Product-oriented: You measure success by dealer and consumer outcomes, not just technical metrics. 

• Reliability- and safety-first: You move fast with guardrails, rollbacks, and clear SLOs. 

• Systems thinker: You design for multi-tenant scale, portability, and cost efficiency. 

• Collaborative: You translate between Applied Sciences, Product, and the Data & AI Platform; you document and teach. 

• Pragmatic: You automate the 80% and leave room for rapid experimentation. 

The Community You Will Join:

The Vision of the POET function is to be the Customer Champion for Payments Engineering by driving the Flow Level Observibility, Operational Excellence & Reliability Capabilities. POET achieves the same by  

• Being the ‘Voice of Customer’ - understanding customer impacting issues through proactive and reactive channels 
• Driving ‘Root-cause fixes’ by working in collaboration with the Engineering, Product & Partnership teams  
• Enabling the ‘Foundational capabilities’ & AI driven Automations that unlock productivity at scale 

The role:

We are looking for a self-motivated engineer who can work in a highly dynamic environment by collaborating with cross-functional and cross-geographical teams. As a Senior Software Engineer, you are expected to bring in-depth technical expertise of building and managing large scale platforms to improve the availability of our Payments platform for all our internal & external stakeholders. You will also be expected to play a critical role in developing and maintaining the tools and systems that enable Airbnb’s engineering teams to operate services reliably and at scale. You will collaborate closely with other Payments engineering teams & Infra teams to ensure services are instrumented, scalable, and resilient to support our growing business.

The Difference You Will Make:

• Design, implement, and maintain systems and tools at a platform level that support Flow level observability, Payments reliability.
• Identify and drive improvements to increase the Payments Availability, Observability, and Resiliency of Airbnb Payments
• Develop observability standards/ framework for new product readiness to ensure service reliability in SOA and distributed systems
• Build Domain Expertise to achieve Scalability - by understanding the nuances of Payments  - across processing, compliance and infra
• Drive large scale migration and adoption projects on Observability & Reliability by cross-collaborating with various Payments teams 
• Lead initiatives that promote a culture of reliability throughout the organization by improving incident management platforms and instrumentation.
• Continuously evaluate new technologies including AI/ML and industry best practices to embed into Observability & Reliability initiatives.
• Drive initiatives independently to fix root-causes identified from the repeat issues observed across monitoring platforms - challenge the status quo and follow through to completion

A Typical Day:

• Build backend services and APIs to create scalable engineering systems 
• Collaborating across engineering, data & operations teams to embed reliability and resilience in service designs
• Enhance system reliability by leading and coordinating RCAs and long-term fixes 
• Influencing architectural decisions toward reliability and scalability
• Collaborate with large set of stakeholders across engineering, infrastructure and operations teams to align and implement foundational & Operational programs 
• Reviewing code and design docs, giving feedback on product specs and mocks. Mentor and guide less-experienced engineers in reliability best practices

Your Expertise:

• 7+ years of experience in back-end software development focusing on large-scale distributed systems.
• BE/B.tech in Computer Science or a related technical field.
• Strong software development skills in one or more languages such as Java / Python / Kotlin / Scala / Ruby on Rails 
• Experience in building intelligent AI agents, and systems powered by Large Language Models is a big plus.
• Evidence of exposure to architectural patterns of a large, high-scale web application (e.g., well-designed APIs, high volume data pipelines, efficient algorithms).
• Familiarity with cloud platforms like AWS or Google Cloud Platform.
• Deep understanding of software development best practices, including version control, automated testing, CI/CD, and code reviews.
• Experience in incident management, monitoring, alerting, and root cause analysis.
• Effective leadership and communication skills to coordinate cross-functional teams during large scale projects
• Experience with initiatives across Auto scaling, Self-healing mechanism, Chaos Engineering, Performance optimization techniques will be a plus. Previous experience in AI/ML will also be a plus. 
• You are a strong problem solver and have worked in a team that is on-call for production systems before
• You are passionate about efficiency, availability, technical quality and system quality
•  

Our Commitment To Inclusion & Belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply.

Role: Principal Architect 

Reports to: Global Head of Technology (SVP Engineering)
Location: Bengaluru (hybrid)
Function: Platform & Product Architecture (Cloud-Native Data/SaaS)

About Reltio

At Reltio®, we believe data should fuel business success. Reltio’s AI-powered data unification and management capabilities—encompassing entity resolution, multi-domain master data management (MDM), and data products—transform siloed data from disparate sources into unified, trusted, and interoperable data. The Reltio Connected Data Platform™ delivers interoperable data where and when it's needed, empowering data and analytics leaders with unparalleled business responsiveness. Leading enterprise brands—across multiple industries around the globe—rely on our award-winning data unification and cloud-native MDM capabilities to improve efficiency, manage risk and drive growth.

At Reltio, our values guide everything we do. With an unyielding commitment to prioritizing our “Customer First”, we strive to ensure their success. We embrace our differences and are “Better Together” as One Reltio. We are always looking to “Simplify and Share” our knowledge when we collaborate to remove obstacles for each other. We hold ourselves accountable for our actions and outcomes and strive for excellence. We “Own It”. Every day, we innovate and evolve, so that today is “Always Better Than Yesterday”. If you share and embody these values, we invite you to join our team at Reltio and contribute to our mission of excellence.

Reltio has earned numerous awards and top rankings for our technology, our culture and our people. Reltio was founded on a distributed workforce and offers flexible work arrangements to help our people manage their personal and professional lives. If you’re ready to work on unrivaled technology where your desire to be part of a collaborative team is met with a laser-focused mission to enable digital transformation with connected data, let’s talk!

Job overview

Reltio is scaling a cloud-native, multi-tenant data platform used by global enterprises. We need a Principal Architect to set the end-to-end architecture vision, de-risk the roadmap, and turn strategy into secure, reliable, cost-efficient systems—while upleveling the engineering organization.

• Enterprise & Product Architecture: Define the north-star architecture for a multi-tenant SaaS data platform (ingest → processing → storage → query → API/SDK/UX). Establish reference architectures, decision records, and standards.
• Cloud-Native Platform: Drive container orchestration, service mesh, control planes, observability, and resilience across AWS/Azure/GCP.
• Data & Streaming: Lead designs for high-volume streaming, low-latency query, and analytical/ML use cases (batch + real-time).
• AI/ML & Agentic AI: Partner with product and security to embed responsible LLM/agentic patterns (guardrails, retrieval, evals) into customer-facing and internal developer workflows.
• Reliability, Security & Compliance: Champion SRE/SOC2/ISO/GDPR-by-design, error budgets, threat modeling, and secure SDLC.
• Developer Experience: Improve CI/CD, test strategy, paved roads, and golden paths; reduce cognitive load and toil across teams.
• Executive & Customer Influence: Translate architecture trade-offs for executives and key customers; guide buy-build-integrate decisions.

Key Responsibilities 

Set the vision: Author and socialize the product/platform architecture strategy; maintain ADRs and tech radars.

• Design at scale: Lead designs for multi-tenant isolation, data models, schemas, indexing/OLAP, and event-driven systems.
• Evolve the platform: Standardize service meshes, API gateways, policy-as-code, and multi-cloud deployment patterns.
• Operational excellence: Embed SRE practices (SLOs, error budgets, incident learning), capacity planning, and chaos testing.
• Security & privacy: Lead threat models, data classification, encryption, secrets, key management, and SBOM/SLSA controls.
• AI architecture: Define patterns for RAG/agents, prompt & model governance, telemetry, evaluations, and fallbacks.
• Mentor & scale: Coach Staff/Principal engineers and architects; chair architecture reviews; build a healthy review culture.
• Executive partnering: Communicate options/trade-offs to C-suite; align decisions with business value and customer impact.
• Customer evangelism: Join key enterprise conversations; convert requirements into pragmatic designs and phased delivery.

Qualifications

• 20+ years in product/platform architecture for large-scale, cloud-native SaaS; recent Principal/Distinguished Architect scope leading end-to-end product decisions and migrations across multi-cloud.
• Cloud & Kubernetes depth: EKS/AKS/GKE; service mesh (Istio/Linkerd), observability (Prometheus/Grafana), platform control planes, operators/CRDs. Able to define golden paths/paved roads for teams.
• Data platform & streaming expertise: Kafka/Flink/Spark; columnar/OLAP (Druid, ClickHouse), NoSQL (Cassandra), search (Elasticsearch/Opensearch); real-time + batch patterns.
• Microservices & integration at enterprise scale: API gateways, event-driven design, SOA/EAI (Apache Camel/Mule/WSO2/JBoss Fuse).
• AI/ML & Agentic AI in production: ML pipelines (MLflow/Kubeflow/Airflow) and LLM/agent frameworks (OpenAI APIs, LLaMA, LangChain/LangGraph; tool-use, memory, evals).
• Security, privacy, and compliance by design: threat modeling, data classification, encryption/keys, and EU GDPR awareness through real product work.
• Executive communication & customer leadership: converts complex trade-offs into simple choices; partners with CXO/stakeholders globally; architecture evangelism with customers.
• C-suite influence & decision facilitation: runs architecture reviews/SteerCos with crisp options, risk/ROI framing, and written decision records; pre-wires global stakeholders to land decisions quickly.
• Customer-back product thinking: meets enterprises regularly, translates requirements into phased designs (MVP → scale), and defends non-negotiables (SLOs, security, privacy).
• Cross-geo orchestration: proven delivery across US/EU/India time zones; builds async rituals, documentation standards, and handoff mechanisms that raise velocity and quality.
• Culture carrier & coaching: uplevels Staff/Principal engineers, codifies paved roads (CI/CD, testing, observability), and scales architectural patterns via ADRs/radars. 

Preferred Qualifications

• Multi-cloud platform stewardship: proven ownership of cross-cloud platforms (e.g., control planes, observability, incident management) and data lake stacks, shipped to enterprise customers.
• Cost/performance engineering: track record reducing infra cost per tenant and improving p95/p99 latency/MTTR via capacity planning, workload placement, and autoscaling patterns.
• Programming fluency: Java/Scala/Python/Go/Rust for credible code-review and reference implementations.
• OSS & ecosystem contribution: prior contributions to relevant frameworks (e.g., Hadoop tuning, NoSQL ORM, Camel/Keycloak) a plus.