What is the InMobi family like?   

We are an infectious bunch. Be it the way we rise to challenges, the innovative products we create, the dreams we chase or the fun we have at work. We are sure that if you meet us, you will be infected too. Today, we are proud to be the leaders in Mobile advertising and are on an accelerated path of being a leader in enterprise software for marketers. We invite you to free yourself, dream big and chase your passion. We are here today because a few of us did just that.   

What can we promise?   

We offer you an opportunity to work on building enterprise platforms that require you to acquire, hone and demonstrate your ML and AI skills in designing and solving for complex problems, thereby creating software that creates value for our customers in a reliable, scalable, secure, and AI-first manner. At InMobi, you get to work on innovative technologies, work closely with business teams and grow as an overall technology leader.  

Modern work environment, flexible schedule, and smart, creative, down-to-earth people. Internal opportunities to move roles and try out bridge assignments with different teams.  Food for your soul – free meals all days of the week, gym, or yoga class to flex those biceps, cocktails at drink cart Thursdays and fun at work on Funky Fridays. We even promise to let you bring your kids and pets to work.  

What is the team like?  

At InMobi Enterprise team, we are transforming the way finance, sales, marketing, people, and legal functions operate. We’re driving transformative work across Sales, Marketing, People, and Finance with ML and Agentic AI—and we’re looking for passionate innovators to join us!

What you will be doing

We are looking for a Data Scientist who can operate at the intersection of classical machine learning, GenAI and modern agentic AI systems with a strong engineering appetite. You will build and deploy intelligent AI/ML systems that will help transform the business process across the core. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.

Your responsibilities will include:

• Lead the experimentation, design and build of agentic AI systems that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
• Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding, content classification, and user preference modeling.
• Design intelligent agents that can automate repetitive decision-making tasks—e.g., candidate generation tuning, feature selection, or context-aware content curation.
• Support POCs/ Pilots projects to explore and validate innovative technologies and solutions in ML/AI space.

• Build and operate machine learning models on diverse, high-volume data sources for forecasting, classification and prediction.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
• Design pipelines for data preparation, model training, evaluation, and deployment in collaboration with engineering counterparts.
• Define best practices for monitoring ML model performance using statistical techniques; identifying drifts, failure modes, and improvement opportunities.
• Contribute to ML/AI thought leadership—blogs, case studies, internal tech talks, and industry conferences.
• Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
• Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.
• Document designs, development processes, and best practices to promote knowledge sharing and operational efficiency.

What do we expect from you?

We are seeking candidates with deep expertise in ML and hands-on experience building GenAI and Agentic AI systems.

Skillset: AI/ML Engineering, Python, LLM, RAG, Agentic AI, MCP, REST API

You should have experience with:

• Design, build, and deploy building LLM-powered applications, chatbots, and multi-agent systems to improve efficiency, accuracy, and decision-making.
• Implement and customize GenAI and multi-agent systems using AI frameworks such as LangChain, LangGraph, CrewAI, or similar.
• Create, refine, and maintain prompt templates, chains, and workflows.
• Integrate vector databases (MilvusDB, FAISS, Pinecone, Chroma, etc.) for semantic search and memory.
• Classical ML and deep learning techniques across NLP, clustering, and time series.
• Proficient in deploying ML workflows/models in production system. Continuous monitoring, evaluation, and retraining of models to ensure sustained performance. Ensuring model explainability, accuracy, compliance.
• Big data processing (Spark, distributed data systems) and cloud computing.
• Designing end-to-end ML solutions—from prototype to production.
• Stay updated with emerging AI/ML technologies and recommend relevant innovations for the finance domain.
• Drive in design reviews, sprint planning, and technical discussions.
• Collaborating effectively with diverse stakeholders, including technical teams and business leaders.
• Adapting to fast-paced environments and evolving priorities with high energy and autonomy.

We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact.

 Qualifications

• Bachelor’s/Master’s in Computer Science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
• 8+ years of industry experience in ML/Data Science, with deep proficiency in Python and one or more frameworks: PyTorch, TensorFlow, Scikit-learn.
• Solid understanding of ML fundamentals, regression, tree-based models, clustering, and time series.
• Hands-on experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
• Experience building Agentic frameworks such as LangGraph, AutoGen, CrewAI, n8n or ReACT-style agents.
• Expertise with algorithms in NLP, Time Series, and Deep Learning, applied on real-world datasets.
• Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
• Experience with Model Context Protocols (MCPs) — building or integrating MCP tools, servers, or capabilities.
• Comfortable working in cross-functional teams.
• Strong problem-solving and analytical skills with the ability to work in agile environments.
• Good understanding of data pipelines, APIs, and cloud platforms (Azure/GCP/AWS).
• Excellent communication skills with the ability to simplify complex technical concepts.

Role

We are looking for a Staff Detection Engineer to join our Zscaler Threat Hunting team, reporting to the Senior Manager, Zscaler Threat Hunting. You will combine threat research and engineering expertise to build the next generation of detections across AWS and cloud infrastructures. By leveraging big data platforms like Hadoop and Athena, you will design scalable hunting logic and automated pipelines to defend our global footprint and enhance threat visibility.

What you’ll do (Role Expectations)

• Combine threat research and engineering expertise to develop advanced detections and hunting logic using Python and AWS infrastructure

• Design, scale, and maintain engineering projects, including GitLab pipelines and repeatable deployment steps to improve hunting efficiency

• Develop YAML-based detections and SIGMA-like rule technologies while migrating existing hunting detections to next-generation systems

• Utilize data platforms like Hadoop, Athena, and data lakes to monitor and test new intelligence sources for enhanced threat visibility

• Independently write detections and playbooks while supporting operational demands, including weekend night shifts and on-call rotations

Who You Are (Success Profile)

• You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.

• You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.

• You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.

• You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.

• You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.

What We’re Looking for (Minimum Qualifications)

• Proven experience in detection engineering, threat hunting, security operations, or malware analysis

• Hands-on experience developing and implementing detection rules in SIEM tools such as Splunk, Microsoft Sentinel, or ElasticSearch

• Deep familiarity with the MITRE ATT&CK framework and experience translating TTPs into actionable detection logic

• Bachelor’s or graduate degree in Computer Science, Engineering, or a related discipline, or equivalent security industry experience

• Ability to work flexible schedules, including weekend night shifts, to meet global operational demands

What Will Make You Stand Out (Preferred Qualifications)

• Significant experience as a Senior Detection Engineer leading complex detection strategies and mentoring junior team members

• Expertise in validating detection logic, performing root cause analysis of failures, and delivering platform improvement recommendations

• Advanced scripting and automation skills in Python for developing and managing complex detection infrastructure

#LI-Hybrid #LI-PM5

About the Role:

This role is responsible for managing and maintaining complex, distributed big data ecosystems. It ensures the reliability, scalability, and security of large-scale production infrastructure. Key responsibilities include automating processes, optimizing workflows, troubleshooting production issues, and driving system improvements across multiple business verticals.

Roles and Responsibilities:

• Manage, maintain, and support incremental changes to Linux/Unix environments.
• Lead on-call rotations and incident responses, conducting root cause analysis and driving postmortem processes.
• Design and implement automation systems for managing big data infrastructure, including provisioning, scaling, upgrades, and patching clusters.
• Troubleshoot and resolve complex production issues while identifying root causes and implementing mitigating strategies.
• Design and review scalable and reliable system architectures.
• Collaborate with teams to optimize overall system performance.
• Enforce security standards across systems and infrastructure.
• Set technical direction, drive standardization, and operate independently.
• Ensure availability, performance, and scalability of systems and services through proactive monitoring, maintenance, and capacity planning.
• Resolve, analyze, and respond to system outages and disruptions and implement measures to prevent similar incidents from recurring.
• Develop tools and scripts to automate operational processes, reducing manual workload, increasing efficiency and improving system resilience.
• Monitor and optimize system performance and resource usage, identify and address bottlenecks, and implement best practices for performance tuning.
• Collaborate with development teams to integrate best practices for reliability, scalability, and performance into the software development lifecycle.
• Stay informed of industry technology trends and innovations, and actively contribute to the organization's technology communities.
• Develop and enforce SRE best practices and principles.
• Align across functional teams on priorities and deliverables.
• Drive automation to enhance operational efficiency.

Skills Required:

• Over 7 years of experience managing and maintaining distributed big data ecosystems.
• Strong expertise in Linux including IP, Iptables, and IPsec.
• Proficiency in scripting/programming with languages like Perl, Golang, or Python.
• Hands-on experience with the Hadoop stack (HDFS, HBase, Airflow, YARN, Ranger, Kafka, Pinot).
• Familiarity with open-source configuration management and deployment tools such as Puppet, Salt, Chef, or Ansible.
• Solid understanding of networking, open-source technologies, and related tools.
• Excellent communication and collaboration skills.
• DevOps tools: Saltstack, Ansible, docker, Git.
• SRE Logging and monitoring tools: ELK stack, Grafana, Prometheus, opentsdb, Open Telemetry.

Good to Have:

• Experience managing infrastructure on public cloud platforms (AWS, Azure, GCP).
• Experience in designing and reviewing system architectures for scalability and reliability.
• Experience with observability tools to visualize and alert on system performance.

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. 

We are seeking an experienced and highly skilled Senior Data Scientist to lead our efforts in optimizing ad display on native Android surfaces. In this role, you will design and implement data-driven strategies to maximize ad effectiveness, user engagement, and revenue. You will collaborate closely with product managers, engineers, and cross-functional teams to drive innovation, improve ad relevance, and ensure a seamless user experience.

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

• Develop and refine data-driven ad optimization strategies tailored to native Android environments, focusing on user engagement, personalization, and revenue uplift.
• Design, implement, and improve predictive models for ad targeting, recommendation, and bidding strategies using cutting-edge machine learning and statistical techniques.
• Lead the design and analysis of experiments to test ad placements, formats, and targeting methods. Use data from A/B tests and multivariate testing to validate hypotheses and inform decisions.
• Analyze user behavior patterns to understand engagement and dropout points, leveraging insights to optimize ad positioning, timing, and relevance.
• Partner with product managers, engineers, designers, and marketing teams to align ad strategies with business objectives and user experience goals. Communicate insights and recommendations effectively to both technical and non-technical stakeholders.
• Ensure the scalability of ad models across a wide range of Android devices and varying network conditions. Work closely with engineering teams to deploy and monitor models in a production environment.
• Provide mentorship and guidance to junior data scientists, fostering a collaborative and innovative team environment. Lead by example in developing high-quality, scalable solutions for complex ad optimization challenges.

You’ll be a good fit if you have:

• PhD (preferred) or Master's Degree (required) in Computer Science, Data Science, Statistics, Machine Learning, or a related field.
• 6+ years of experience in data science or machine learning, with a strong focus on ad technology or user experience optimization.
• Demonstrable experience in working with ad serving, ad tech stacks, and optimization techniques, especially within Android or mobile ecosystems.
• Expertise in machine learning frameworks (TensorFlow, PyTorch, or similar).
• Advanced proficiency in Python, or Kotlin.
• Experience with data processing and pipeline tools (e.g., Spark, Hadoop, Airflow).
• Deep understanding of A/B testing, causal inference, and other experimentation methods.
• Familiarity with ad tech, programmatic advertising, and user behavior metrics, particularly in the context of mobile and Android platforms.
• Excellent analytical, problem-solving, and communication skills. Proven ability to articulate complex data science concepts to stakeholders across different domains.

 Nice to have: 

• Previous experience optimizing ad displays on native mobile or Android surfaces.
• Familiarity with the Android SDK and understanding of native Android UI/UX design principles.
• Knowledge of reinforcement learning, multi-armed bandit models, and contextual targeting techniques.

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

Role Summary

We are building a next-generation Core Infrastructure platform focused on:

• Zero-trust security and identity-based access
• Multi-region and multi-account scalability (multi cloud in future)
• Highly automated, self-service infrastructure
• Reliable and observable systems at scale

This role will own foundational infrastructure systems—networking, identity, compute platforms, and automation frameworks—that power all services.

Core Areas of Ownership

[1] Core Infrastructure & Cloud Platform

• Design and evolve infrastructure on:
• Amazon Web Services
• Kubernetes
• Build and scale:
• Multi-region and multi-account architectures
• Secure and scalable service connectivity (VPC, cross-account, cross-region)

[2] Security & Zero Trust

• Drive adoption of:
• Identity-based access (eliminate shared credentials)
• Implement:
• Secrets management using HashiCorp Vault
• Policy enforcement using Open Policy Agent
• Design:
• Secure service-to-service communication
• Fine-grained IAM and access control systems

[3] Infrastructure as Code & Automation

• Build infrastructure using:
• Terraform
• Pulumi
• Ansible
• Create:
• Reusable modules and abstractions
• Fully automated provisioning workflows
• Integrate with CI/CD:
• GitHub Actions
• Jenkins

[4] Cluster & Infrastructure Lifecycle

• Own lifecycle of:
• Kubernetes clusters (e.g., EKS)
• Databases (eg, rds/aurora/elastic cache. etc)
• Automate:
• Cluster upgrades and migrations
• Node scaling and patching

[5] Multi-Region & Migration Engineering

• Design:
• Region-agnostic infrastructure patterns
• Enable:
• Automated regions bootstrap and failover
• Lead:
• Migration initiatives (region, account, or cloud)

[6] Observability & Reliability

• Build and operate:
• Metrics, logging, and tracing systems using:
• Prometheus
• Grafana
• ELK stack
• Datadog
• Improve:
• System reliability, alerting, and incident response

[7 ] Governance & Standards

• Define and enforce:
• Resource tagging strategies
• Data classification policies
• Ensure:
• Cost visibility and auditability

[8] Developer Productivity & Platform

• Build:
• Self-service infrastructure workflows
• Improve:
• Developer experience through automation and tooling
• Contribute to:
• Internal platform evolution

Responsibilities

• Architect, build, and scale core infrastructure systems
• Automate infrastructure lifecycle to minimize human intervention
• Design secure connectivity across services, VPCs, accounts, and regions
• Debug and resolve complex production issues across the stack
• Write production-quality code, tools, and frameworks
• Collaborate with engineering teams to standardize infrastructure usage

Minimum Qualifications

• 10+ years in a Software Engineering role or equivalent experience
• 5+ years of experience in infrastructure engineering
• Strong hands-on experience with:
• Amazon Web Services
• Kubernetes
• Terraform or Pulumi
• Strong understanding of:
• Networking (VPC, routing, DNS, connectivity)
• IAM and access control
• Experience with:
• Automating infrastructure workflows
• High-availability and scalable systems
• Proficiency in:
• Python / Go / Bash

Preferred Qualifications

• Experience with:
• Multi-region or multi-account architectures
• Automating infrastructure migrations and upgrades (e.g., EKS upgrades)
• Secrets management platforms (e.g., HashiCorp Vault)
• Familiarity with:
• Policy-as-code (e.g., Open Policy Agent)
• Observability systems (Prometheus, Grafana, datadog)
• Exposure to:
• Data infrastructure (Hadoop, Trino, Spark)
• Service mesh (e.g., Istio)

What you will be doing

We are looking for a Data Scientist who can operate at the intersection of classical machine learning, large-scale recommendation systems, and modern agentic AI systems.

You will design, build, and deploy intelligent systems that power Glance’s personalized lock screen and live entertainment experiences. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.

Your responsibilities will include:

Classical ML & Recommendation Systems

• Design and develop large-scale recommendation systems using advanced ML, statistical modeling, ranking algorithms, and deep learning.
• Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
• Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts.
• Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities.

Agentic Systems & Next-Gen AI

• Build and experiment with agentic AI systems that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
• Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding, content classification, and user preference modeling.
• Design intelligent agents that can automate repetitive decision-making tasks—e.g., candidate generation tuning, feature selection, or context-aware content curation.
• Explore reinforcement learning, contextual bandits, and self-improving systems to power next-generation personalization.

Cross-functional impact

• Collaborate with Designers, UX Researchers, Product Managers, and Software Engineers to integrate ML and GenAI-driven features into Glance’s consumer experiences.
• Contribute to Glance’s ML/AI thought leadership—blogs, case studies, internal tech talks, and industry conferences.
• Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
• Plus: Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.

The experience we need

We are seeking candidates with deep expertise in ML, recommendation systems, and a strong appetite for building agentic AI systems.

You should have experience with:

• Large-scale ML and recommendation systems (collaborative filtering, ranking models, content-based approaches, embeddings).
• Classical ML and deep learning techniques across NLP, sequence modeling, RL, clustering, and time series.
• Experience in deploying ML workflows/models in production system
• Big data processing (Spark, distributed data systems) and cloud computing.
• Designing end-to-end ML solutions—from prototype to production.
• Plus: Building or experimenting with LLMs, generative models, and agentic AI workflows (e.g., autonomous evaluators, self-improving pipelines, automated experiment agents).

We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact.

Qualifications

• Bachelor’s/Master’s in Computer Science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
• 6+ years of industry experience in ML/Data Science, ideally in large-scale recommendation systems or personalization.
• Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
• Expertise with algorithms in NLP, Reinforcement Learning, Time Series, and Deep Learning, applied on real-world datasets.
• Proficient in Python and comfortable with statistical tools (R, NumPy, SciPy, PyTorch/TensorFlow, etc.).
• Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
• Comfortable working in cross-functional teams.
• Familiarity with privacy-preserving ML and identity-less ecosystems (especially on iOS and Android).
• Excellent communication skills with the ability to simplify complex technical concepts.

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.