Job Description

We’re seeking a hands-on Senior Data Architect to design and modernize scalable, AI-ready data platforms across cloud. The ideal candidate has deep technical expertise in AWS, PySpark, and Python, and experience migrating legacy Java-based scripts to Spark frameworks while ensuring data quality, scalability, and performance.

Key Responsibilities

• Design and implement data architecture and pipelines using PySpark, Python, and AWS services (Glue, EMR, S3, Redshift, Athena).
• Modernize legacy systems by converting Java-based migration or ETL scripts into Spark-based solutions.
• Build and manage data lakes and data warehouses optimized for analytics and AI/ML workflows.
• Collaborate with data scientists and analysts to enable model training, deployment, and continuous data delivery.
• Define and document HLD (High-Level Design) and LLD (Low-Level Design) for scalable data systems.
• Implement standards for data quality, governance, performance optimization, and security.
• Lead troubleshooting, performance tuning, and architecture reviews for data pipelines.

Skills and Experience

• 15+ years of experience in data engineering or architecture, with strong hands-on development background.
• Expert-level proficiency in PySpark, Python, and SQL for data processing and transformation.
• Strong understanding of AWS data ecosystem — including Glue, EMR, S3, Redshift, and Lambda.
• Proven experience migrating or re-engineering Java-based ETL systems into modern Spark environments.
• Solid foundation in data lake, data warehouse, and cloud-native architecture design.
• Experience supporting AI/ML data pipelines and integrating data platforms with ML workflows.
• Strong documentation skills (HLD, LLD) and ability to communicate architecture clearly to technical and business stakeholders.
• Experience with both RDBMS such as Maria DB/MySQL and No-SQL database such MongoDB
• Experience with GIT, Jenkins, & AWS
• Expertise in performance optimization and cost management for cloud environments
• Ability to manage multiple projects and priorities in a fast-paced environment
• AWS Professional level certifications (e.g., Solutions Architect Professional, DevOps Engineer Professional

Role Overview
We are seeking a Staff Frontend Engineer to be the technical anchor for our frontend capability. This role goes beyond feature delivery — you will define the frontend architecture, establish the component design system, own the Next.js application structure, drive performance standards, and mentor Senior and mid-level frontend engineers. You will build the most complex, data-intensive UI surfaces: real-time attribution dashboards, AI powered campaign builders, and platform-embedded intelligence widgets — all in React + Next.js stack integrated with Elasticsearch-powered search and streaming AI responses.  

Key Responsibilities
• Own and evolve the frontend architecture: Next.js App Router structure, component boundaries, state management strategy (Redux), and data-fetching patterns — applying hexagonal UI principles where the component layer is decoupled from specific backend service contracts.

• Design and build the most technically complex UI surfaces: real-time attribution dashboards (streaming data), AI-powered campaign builder (LLM response streaming), Elasticsearch-backed customer search with faceted filtering, and platform-embedded opportunity cards.

• Define the component design system: atomic component library, theming contracts, accessibility standards (WCAG 2.1), and Storybook-based documentation.

• Establish frontend performance standards: Core Web Vitals targets, bundle size budgets, SSR/SSG strategy with Next.js, lazy loading, and virtualization for large data tables.

• Lead Next.js architecture decisions: App Router vs. Pages Router tradeoffs, RSC (React Server Components) applicability, streaming SSR for AI-generated content, and API route design.

• Integrate AI-native frontend patterns: streaming LLM responses into UI components, real-time score updates from Redis/Aerospike via SSE, and Elasticsearch typeahead search. 

• Drive code quality through design reviews, code reviews, pair programming, and frontend-specific engineering standards.

• Mentor Senior and mid-level frontend engineers — setting the bar for component design, test coverage, and performance discipline.

• Collaborate closely with backend Staff Engineers to define API contracts and with designers to implement pixel-perfect, accessible UIs

Skills & Qualifications

• 8+ years of frontend engineering experience with deep React and Next.js expertise in production systems.

• Mastery of Next.js 14+ App Router, React Server Components, streaming SSR, and static generation — knowing when to apply each pattern.

• Strong TypeScript expertise: type safety across component props, API contracts, Redux store shape, and server/client boundaries.

• Deep Redux expertise: slice design, selector optimization, middleware patterns, and RTK Query for data fetching.

• Proven experience building data-intensive dashboards with real-time data (WebSocket, SSE, or polling) and chart libraries (Recharts, D3.js).

• Experience integrating Elasticsearch-powered search UIs: typeahead, faceted search, and result ranking display.

• Strong frontend performance engineering: bundle analysis, code splitting, memoization, virtualization, and Core Web Vitals optimization.

• Experience establishing and maintaining component design systems with accessibility standards.

• Experience mentoring engineers and raising the technical bar across a frontend team. 

Good to Have

• Experience streaming LLM/AI-generated content into React UI components (token streaming, partial rendering patterns).

• Familiarity with platform-embedded micro-frontend patterns (module federation or iframe-based embedding).

• Background in automotive retail, eCommerce analytics, or SaaS BI/reporting platforms.

• Experience with Storybook for component documentation and visual regression testing. 

Role: GenAI Lead Engineer

Experience: 8+ years total (with significant hands-on GenAI / LLM work)

Job Overview:

We are seeking an innovative and highly skilled Lead Generative AI (GenAI) Engineer to spearhead the design, development, and deployment of advanced AI-powered solutions. In this role, you will lead a team of engineers and data scientists to harness cutting-edge Generative AI technologies and implement them to solve complex business problems, enhance user experiences, and drive innovation. This role combines deep technical expertise, leadership, and a strong understanding of AI trends and tools.

Key Responsibilities:

• Technical Leadership:
• Lead the end-to-end design and implementation of Generative AI solutions.
• Provide technical guidance and mentorship to engineers and data scientists working on GenAI projects.
• Stay updated with the latest trends, research, and advancements in Generative AI and Large Language Models (LLMs).
• Solution Development:
• Architect, train, and fine-tune state-of-the-art LLMs and generative AI models
• Develop and optimize pipelines for prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning.
• Develop and deploy generative AI models, particularly focusing on ChatGPT, using Python on Azure or AWS Platform or .Net on Azure platform
• Ensure scalability, performance, and security of AI solutions deployed in production.
• Integration and Deployment:
• API Development: Ability to define and deliver API access for GenAI services, facilitating integration with other systems and applications.
• Collaborate with software engineering teams to integrate GenAI solutions into enterprise applications and services.
• Utilize cloud platforms (e.g., Azure, AWS, or GCP) to deploy and manage AI models and APIs.
• Leverage MLOps practices for continuous model monitoring, retraining, and improvement.
• Data Strategy and Preparation:
• Collaborate with data engineering teams to ensure high-quality data acquisition, preprocessing, and augmentation for model training and fine-tuning.
• Implement data governance and privacy practices in line with organizational policies.
• Innovation and Research:
• Experiment with new generative AI techniques, such as multimodal AI, reinforcement learning with human feedback (RLHF), and active learning.
• Evaluate and recommend AI frameworks, libraries, and platforms for project requirements.
• Stakeholder Collaboration:
• Work closely with product managers, business stakeholders, and UX designers to define AI-powered product features and use cases.
• Present technical concepts, project progress, and AI capabilities to non-technical audiences.

Key Requirements

• Technical Skills:
• Hands-on experience with cloud platforms and services for AI/ML, such as Azure AI Services, Azure Machine Learning, AWS Bedrock, or Google Vertex AI.
• Hands on experience in any of LLMs such as OpenAI’s ChatGPT Models , Gemini, Llama 2 ,Claude 2 ,Grok
• Hands on experience in any of the agentic frameworks like LangChain, Semantic kernel, AutoGen, CrewAi
• Hands on experience using any of vector database like Chroma, Pinecone, Weaviate, Faiss
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF)
• Strong expertise in LLMs and generative AI frameworks like OpenAI, Hugging Face Transformers, or similar platforms.
• Deep understanding of natural language processing (NLP) concepts, including tokenization, embeddings, and sequence-to-sequence models.
• Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Experience in CI/CD pipeline management and automation tools, particularly within the Azure DevOps environment. Knowledge of containerization (e.g., Docker) and orchestration tools is also important
• Familiarity with MLOps tools and practices, such as MLflow, Kubeflow, or Docker.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. preferred).
• 8+ years of experience in AI/ML engineering, with 2+ years specifically in Generative AI.
• Minimum of 2 years of experience in building Conversational AI applications using cloud-based services and in orchestrating AI/ML services for building a complete solution
• Minimum of 6 years of extensive full-time experience in Data Analysis, Statistics, Machine Learning, or Computer Science
• Proven track record of leading AI projects from inception to production.
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF).
• Certifications in AI/ML or cloud platforms (e.g., Azure AI Engineer, AWS Certified Machine Learning).

Job Description

You will develop enterprise-grade frontend experiences embedded within our core platform and collaborate closely with designers, backend engineers, and product managers to deliver a world-class, AI-native user experience. You will build scalable, performant React + Next.js applications that surface real-time intelligence, campaign management interfaces, and data-rich dashboards — deeply integrated with our backend services and platform component framework.

Key Responsibilities
• Design and build responsive, reusable UI components using ReactJS and Next.js — including data visualization dashboards, campaign management interfaces, and customer intelligence widgets.
• Develop large-scale, cloud-ready frontend applications integrated with backend REST APIs, real-time data streams, and Elasticsearch-powered search experiences.
• Manage application state using Redux, ensuring seamless synchronization with backend microservices.
• Build platform-embedded frontend components following the hexagonal UI principle — components are independent, composable, and decoupled from specific data sources via port interfaces.
• Leverage Next.js SSR and SSG capabilities where appropriate for performance-critical views and SEO relevant surfaces.
• Collaborate with product, design, and backend teams to rapidly prototype and deliver high-quality, data-rich features.
• Ensure usability, performance, security, and visual consistency across environments and devices.
• Diagnose and fix UI bugs, rendering performance issues, and frontend bottlenecks.
• Write high-quality, maintainable code with thorough unit and integration tests.
• Follow Tekion UI, design, and engineering best practices.

Skills & Experience
• 3-5 years of experience in frontend engineering; Bachelor's degree in Computer Science or a related field.
• Strong hands-on expertise with React, Next.js, JavaScript/TypeScript, and Redux.
• Solid knowledge of HTML, CSS, and web standards (REST, HTTP, JSON, DOM, W3C).
• Experience building enterprise-grade SaaS applications with real-time data and complex state management requirements.
• Familiarity with Next.js App Router, SSR, SSG, and API routes.
• Excellent understanding of frontend performance: code splitting, lazy loading, memoization, virtualization.
• Experience with Git and project management tools (e.g., JIRA).
• Proven experience delivering scalable, high-performance frontend systems with thorough test coverage.
• Strong sense of ownership and bias for action in a fast-paced startup environment.

Preferred Skills
• Experience integrating AI-generated content and LLM-streamed responses into frontend components.
• Familiarity with data visualization libraries (Recharts, D3.js) for analytics and reporting dashboards.
• Experience building embedded micro-frontend components within larger platform shells.
• Exposure to Elasticsearch-powered search UI patterns (faceted search, typeahead, result ranking).

Perks & Benefits
• Competitive compensation and generous stock options.
• Medical insurance coverage.
• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.

Job Description
You will develop enterprise-grade frontend experiences embedded within our core platform and collaborate closely with designers, backend engineers, and product managers to deliver a world-class, AI-native user experience. You will build scalable, performant React + Next.js applications that surface real-time intelligence, campaign management interfaces, and data-rich dashboards — deeply integrated with our backend services and platform component framework.

Key Responsibilities
• Design and build responsive, reusable UI components using ReactJS and Next.js — including data visualization dashboards, campaign management interfaces, and customer intelligence widgets.
• Develop large-scale, cloud-ready frontend applications integrated with backend REST APIs, real-time data streams, and Elasticsearch-powered search experiences.
• Manage application state using Redux, ensuring seamless synchronization with backend microservices.
• Build platform-embedded frontend components following the hexagonal UI principle — components are independent, composable, and decoupled from specific data sources via port interfaces.
• Leverage Next.js SSR and SSG capabilities where appropriate for performance-critical views and SEO relevant surfaces.
• Collaborate with product, design, and backend teams to rapidly prototype and deliver high-quality, data-rich features.
• Ensure usability, performance, security, and visual consistency across environments and devices.
• Diagnose and fix UI bugs, rendering performance issues, and frontend bottlenecks.
• Write high-quality, maintainable code with thorough unit and integration tests.
• Follow Tekion UI, design, and engineering best practices.

Skills & Experience
• 5+ years of experience in frontend engineering; Bachelor's degree in Computer Science or a related field.
• Strong hands-on expertise with React, Next.js, JavaScript/TypeScript, and Redux.
• Solid knowledge of HTML, CSS, and web standards (REST, HTTP, JSON, DOM, W3C).
• Experience building enterprise-grade SaaS applications with real-time data and complex state management requirements.
• Familiarity with Next.js App Router, SSR, SSG, and API routes.
• Excellent understanding of frontend performance: code splitting, lazy loading, memoization, virtualization.
• Experience with Git and project management tools (e.g., JIRA).
• Proven experience delivering scalable, high-performance frontend systems with thorough test coverage.
• Strong sense of ownership and bias for action in a fast-paced startup environment.

Preferred Skills
• Experience integrating AI-generated content and LLM-streamed responses into frontend components.
• Familiarity with data visualization libraries (Recharts, D3.js) for analytics and reporting dashboards.
• Experience building embedded micro-frontend components within larger platform shells.
• Exposure to Elasticsearch-powered search UI patterns (faceted search, typeahead, result ranking).

Perks & Benefits
• Competitive compensation and generous stock options.
• Medical insurance coverage.
• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.

Role Overview

We are looking for a Senior Backend Engineer to design, build, and scale robust backend services that power complex business workflows. This role requires strong ownership, deep Java expertise, a solid grounding in hexagonal architecture, and the ability to collaborate effectively across product, QA, AI/ML, and engineering teams in a fast-moving, AI-native environment.

Key Responsibilities

• Design and build Java (Spring Boot 3.x) microservices following hexagonal architecture — clean domain logic isolated from MongoDB, PostgreSQL, Cosmos DB, and Kafka adapter implementations.

• Develop and maintain REST APIs, Kafka event consumers/producers, and domain service logic for campaign, scoring, attribution, and customer profile workflows.

• Build and manage MongoDB document schemas and PostgreSQL relational schemas for high-throughput, multi-tenant use cases. 

• Implement Cosmos DB data models for event and state storage with appropriate partition key and consistency strategies.

• Take end-to-end ownership of components — from domain design and hexagonal adapter implementation through deployment and ongoing optimization.

• Integrate with Elasticsearch for campaign search, customer lookup, and full-text query workloads.

• Configure and tune Redis or Aerospike for frequency cap enforcement, scoring cache, and session state management.

• Identify and resolve performance bottlenecks across services, event pipelines, and data layers.

• Participate actively in design discussions, architecture decisions, and code reviews.

• Mentor Junior engineers and act as a role model for code quality, hexagonal design, and AI-native engineering best practices.

Skills & Qualifications

• 5+ years of backend engineering experience; Bachelor's or Master's degree in Computer Science or a related field.

• Strong Java 21 and Spring Boot 3.x expertise; solid grasp of hexagonal architecture and clean domain modeling.

• Experience with Kafka: consumer/producer patterns, at-least-once delivery, idempotent processing, and event-driven service design.

• Hands-on MongoDB experience: document schema design, indexing, aggregation pipelines, multi-tenant isolation.

• PostgreSQL experience: schema design, query optimization, migration management.

• Familiarity with Cosmos DB or equivalent distributed key-value / document stores.

• Experience with Elasticsearch for search and analytics workloads.

• Experience with Redis or Aerospike for high-throughput caching patterns.

• Familiarity with cloud-based systems on AWS (EKS, MSK, S3).

• Strong debugging, performance tuning, and system optimization skills.

• Familiarity with Agile/Scrum development practices and CI/CD pipelines.

Good to Have

• Experience with Temporal for workflow orchestration.

• Familiarity with AI-native integration patterns: MCP tool registration, scoring API contracts, LLM gateway usage.

• Working knowledge of Python for understanding AI/ML service outputs and integrating scoring results.

• Background in enterprise-scale SaaS platforms: eCommerce, supply chain, or similar. 

Job Description

We are looking for a Software Engineer II to join one of our Scrum teams building AI-native, event-driven services for automotive retail intelligence. This is a core contributor role for engineers with 2–4 years of experience who are ready to take end-to-end ownership of features, grow their distributed systems skills, and work in a high velocity environment that demands code quality, hexagonal architecture discipline, and genuine curiosity about AI-native engineering.

Key Responsibilities

• Develop and maintain Java (Spring Boot 3.x) backend services — implementing domain logic, REST APIs, Kafka event consumers/producers, and MongoDB/PostgreSQL persistence adapters following hexagonal architecture patterns.
• Build features across the full backend slice: domain model, port interface, adapter implementation, unit tests, and integration tests.
• Write integration tests using Testcontainers for MongoDB, PostgreSQL, Kafka, and Elasticsearch adapters — ensuring each adapter is tested independently from the domain core.
• Participate in design discussions, architecture reviews, and code reviews — actively learning from Staff and Senior engineers.
• Investigate and resolve bugs, performance issues, and Kafka consumer lag across services.
• Write clean, well-documented, testable code with consistent application of hexagonal design principles.
• Contribute to golden dataset test scenario design under the guidance of the Product Manager and SDET.
• Operate with ownership and curiosity in a fast-paced, startup environment.

Skills and Experience

• 2–4 years of backend engineering experience; Bachelor's or Master's degree in Computer Science or a related field.
• Solid Java fundamentals: object-oriented design, Spring Boot, REST API implementation, and unit testing (JUnit 5).
• Working knowledge of at least one of: MongoDB, PostgreSQL, or Kafka — with willingness to develop depth across all three.
• Understanding of hexagonal architecture concepts (ports & adapters) — ability to implement adapters under the guidance of senior engineers.
• Familiarity with cloud environments (AWS), containerization (Docker), and CI/CD pipelines.
• Strong problem-solving skills, attention to code quality, and ability to receive and apply feedback quickly.
• Collaborative mindset and clear communication — comfortable asking questions and flagging blockers early.

Preferred Skills

• Exposure to Kafka producers and consumers in a production or learning context.
• Familiarity with Elasticsearch for search and analytics use cases.
• Curiosity about AI/ML systems — awareness of what scoring models, embeddings, and LLMs do at a systems level.
• Exposure to Redis or Aerospike for caching patterns.
  
  

Perks and Benefits

• Competitive compensation and generous stock options.
• Medical insurance coverage.
• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.

Job Description

We are seeking a Staff Backend Engineer to design and build scalable, AI-native backend services that power customer intelligence, campaign orchestration, and attribution at automotive retail scale. This role is the critical technical anchor — defining hexagonal architecture patterns, owning service boundaries, driving AI integration patterns, and setting the engineering bar for the entire team.

Key Responsibilities

 • Architect Java (Spring Boot 3.x) microservices using hexagonal architecture — defining clean domain cores, port interfaces for persistence (MongoDB, PostgreSQL, Cosmos DB), and adapter implementations for Kafka, Elasticsearch, Redis/Aerospike, and external APIs.

• Own core domain models and service boundaries end-to-end: from initial hexagonal design through production operation, Kafka event contracts, and iterative enhancement.

• Build scalable, secure REST APIs and robust Kafka-based event-driven pipelines that consume and produce events across multiple source systems. 

• Design MongoDB document schemas and PostgreSQL relational schemas for high-throughput, multi-tenant workloads — with deep attention to indexing strategy, TTL, and tenant isolation patterns.

• Design Cosmos DB data models for high-throughput event and state storage — partition key strategy, consistency levels, and TTL configuration. 

• Design and implement durable campaign execution workflows using Temporal. 

• Establish and own AI-native integration patterns: MCP tool registration, scoring model serving contracts, LLM gateway integration, and agentic workflow design.

• Identify and resolve performance bottlenecks across services, Kafka pipelines, data layers, and Elasticsearch query performance. 

• Ensure all systems meet reliability, scalability, and observability standards.

• Mentor senior, mid-level, and junior engineers — setting and consistently raising the engineering bar.

Skills & Qualifications

• 8+ years of backend engineering experience with a strong track record delivering production-grade Java systems.

• Deep expertise in hexagonal architecture (ports & adapters) — proven ability to design domain cores fully decoupled from infrastructure layers.

• Strong Java 21 and Spring Boot 3.x expertise with event-driven, microservices-first design. 

• Strong experience with Kafka — consumer groups, partition design, offset management, idempotent consumers, and event-driven service integration.

• Proven experience with MongoDB: document schema design, indexing, aggregation pipelines, and multi tenant isolation patterns.

• Strong PostgreSQL experience: complex schema design, query optimization, migration management.

• Experience with Cosmos DB or equivalent globally distributed key-value stores: partition key design, consistency tuning, high-throughput read/write patterns.

• Solid experience with Elasticsearch: index design, search query optimization, relevance tuning.

• Experience with Redis or Aerospike for high-throughput caching and session/state management.

• Strong understanding of distributed systems: service boundaries, data consistency, idempotency, retries, and circuit breaking on AWS (EKS, MSK).

Preferred Skills

• Experience with Temporal for durable workflow orchestration. 

• Familiarity with MCP tool registration patterns and agentic platform integration.

• Working knowledge of Python for AI/ML pipeline integration (scoring models, feature serving, LLM gateway patterns).

• Prior experience building data platforms, real-time analytics systems, or AI-native SaaS products.

Perks & Benefits 

• Competitive compensation and generous stock options.

• Medical insurance coverage.

• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.

Role Overview

We are seeking a Staff Technical Program Manager to own the cross-team delivery machinery for a 20-person AI native engineering organization. This is not a project coordination role — it is a technical program ownership role.  You will maintain the master delivery plan across two Scrum teams and shared resources, track and resolve  
cross-team dependencies (platform teams, ARC backend, OEM integrations), manage risk before it becomes a miss, and create the delivery transparency that allows Engineering, Product, and Executive leadership to make fast, informed decisions. The best TPMs are invisible when things go well and indispensable when they don't. 

Key Responsibilities 

• Own the master delivery plan: maintain the integrated roadmap across both Scrum teams, shared resources (Architect, ML, SDET), and external platform dependencies — with milestone-level clarity at all times.  
• Track and resolve cross-team dependencies: Agentic Engine, Core Platform (Temporal, MCP Gateway, Component Framework), ARC backend (Kafka event contracts, API dependencies), Design (component specs), and external vendors (email/SMS providers).  
• Run the delivery rhythm: sprint review calendar, cross-team sync cadence, dependency review meetings, and risk escalation forums — keeping the Director and VP Engineering informed without burying them in status updates.  
• Identify, quantify, and manage delivery risks: surface blockers early, drive resolution with urgency, and maintain a live risk register with mitigation owners and timelines.  
• Own cross-team technical communication: translate engineering status into language that Product, Business, and Executive stakeholders can act on — and translate business urgency back into engineering trade-off decisions.  
• Drive the release process: define release criteria, coordinate go/no-go decisions with Engineering, QA (Staff SDET), and Product, and manage production deployments across services.  
• Manage the dependency interface to the T1 Agentic Engine team: track which Intent, Skill, Policy, and MCP tool registrations are ready, which are blocked, and what the critical path impact is.  
• Continuously improve the delivery system: identify bottlenecks in the engineering process, drive retrospectives, and implement changes that sustainably increase throughput and quality.

Skills & Qualifications 

• 12+ years of technical program management experience, with at least 3 years at Staff level managing multi team programs at SaaS or platform companies.  
• Strong technical depth: able to read architecture diagrams, understand Kafka dependency graphs, evaluate API contract blockers, and participate credibly in engineering design discussions.  
• Proven experience managing complex, multi-team delivery programs with many external dependencies — including platform dependencies outside your direct control.  
• Expert facilitation of engineering delivery rituals: sprint planning, dependency reviews, release gates, post mortems, and architecture syncs.  
• Deep understanding of AI-native product delivery: what makes AI system delivery different from traditional software (eval requirements, model versioning, non-deterministic outputs, golden dataset dependencies).  
• Excellent written communication: can produce a crisp, executive-readable status update that tells the truth clearly and drives decisions.  
• Strong stakeholder management across Engineering, Product, Design, and Executive levels — comfortable managing up, across, and down.  

 
Good to Have  

• Experience managing delivery programs that involve both Java microservices and Python AI/ML engineering teams simultaneously.  
• Familiarity with agentic AI platform concepts: intent registries, MCP tools, skill/policy authoring, and eval frameworks — enough to track technical dependency status accurately.  
• Background in automotive retail technology or dealer management systems.  
• Prior experience working in a US-India distributed engineering organization.  
 

Job Overview:

We are seeking an experienced Senior Data Engineer to build and optimize scalable data platforms using Microsoft Fabric, Databricks (and/or Snowflake). The role focuses on designing reliable data pipelines, lakehouse and warehouse models, and semantic layers that enable enterprise analytics, BI, and AI/Gen AI use cases.

You will work closely with analytics, BI, and data science teams to deliver high-quality, performant, and governed data solutions, while driving best practices in data engineering, optimization, and platform design.

Key Responsibilities

• Design, build, and maintain end-to-end data solutions on Microsoft Fabric, Databricks, including Pipelines, Notebooks, Lakehouse, Data Warehouse, and Semantic Models.
• Implement scalable data ingestion, transformation, and loading (ETL/ELT) using Fabric Pipelines and PySpark.
• Develop robust data models and schemas optimized for analytics, reporting, and AI-driven consumption.
• Create and maintain semantic models to support Power BI and enterprise BI solutions.
• Engineer high-performance data solutions that meet requirements for throughput, scalability, quality, and security.
• Author efficient PySpark and SQL code for large-scale data transformation, data quality management, and business rule processing.
• Build reusable framework components for metadata-driven pipelines and automation.
• Optimize Lakehouse and Data Warehouse performance, including partitioning, indexing, Delta optimization, and compute tuning.
• Develop and maintain stored procedures and advanced SQL logic for operational workloads.
• Design and prepare feature-ready datasets for AI and GenAI applications.
• Collaborate with data scientists and ML engineers to productionize AI pipelines.
• Implement data governance and metadata practices required for responsible AI.
• Leverage Fabric, Databricks capabilities to orchestrate and monitor AI-related data workflows.
• Apply data governance, privacy, and security standards across all engineered assets.
• Implement monitoring, alerting, and observability best practices for pipelines and compute workloads.
• Drive data quality initiatives, including validation frameworks, profiling, and anomaly detection.
• Partner with analytics, BI, data science, and product teams to understand requirements and translate them into technical solutions.
• Mentor junior engineers and contribute to engineering standards and patterns.
• Participate in architecture reviews, technical design sessions, and roadmap planning.
• Develop and deliver dashboards and reports using Power BI and Tableau.

Required Skills:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
• 7+ years of professional experience in data engineering or a related discipline.
• Mandatory hands-on expertise with Microsoft Fabric, Databricks or Snowflake including:
  • Pipelines
  • Notebooks
  • Lakehouse
  •  Data Warehouse
  • Semantic Models
• Advanced proficiency in PySpark for distributed data processing.
• Strong command of SQL for analytical and operational workloads.
• Experience developing and optimizing stored procedures and complex SQL transformations.
• Understanding of GenAI architectures, vectorization, embedding pipelines, and data preparation for LLM use cases.
• Strong knowledge of data modeling, ETL/ELT patterns, and modern data lakehouse principles.
• Experience designing and optimizing large-scale data pipelines in cloud environments (Azure preferred).
• Excellent problem-solving skills with the ability to analyze complex data workflows.
• Proficiency in creating interactive dashboards and reports using Power BI and Tableau.

• Strong experience creating and deploying cloud infrastructure on Azure, AWS, and GCP
• Understanding of Python and Docker for developing web APIs
• Understanding of deploying solutions to Kubernetes.
• Familiarity with serverless technologies across functions, queues, file storage, API management, and key‑value stores
• Experience with integration patterns and working with various APIs
• Knowledge of DevOps principles and Git, preferably with experience in modern techniques such as monorepos
• Proficiency in Infrastructure‑as‑Code languages such as Terraform and CloudFormation
• Understanding of design patterns for building scalable and efficient systems
• A quality‑driven, test‑first mindset
• Excellent written and verbal communication skills
• Familiarity with Diagrams‑as‑Code for documenting infrastructure architecture is a plus
• Understanding of modern authentication protocols such as OAuth2 and OIDC
• Consideration of cross‑cutting concerns like observability and security in infrastructure design
• Contributions to open‑source projects are a plus

We’re ideally looking for someone who is comfortable across the entire stack and can take ownership of feature delivery from design to deployment. The ideal candidate should have:

• 5+ years of experience with Python and FastAPI (async programming)
• Experience with Redis caching patterns and cache invalidation strategies
• Experience with event-driven architectures (Pub/Sub, SNS/SQS, or similar message brokers)
• Familiarity with SQLAlchemy ORM and relational database patterns
• Experience writing comprehensive test suites (pytest, integration testing)
• Comfortable with REST API design and microservices communication patterns
• Experience with AI-assisted development tools (Cursor, Copilot, Claude Code)
• Experience with Git and CI/CD pipelines

Nice to Have

• Experience with Strapi CMS or similar headless CMS platforms
• Experience with both GCP and AWS cloud services
• Familiarity with i18n patterns (locale handling, translation key management)
• Experience in Java and typescript

Job Description

We are looking for a Senior QA Engineer / SDET to drive quality across our AI-native backend services and platform systems. You will own test automation strategy, release quality gates, and quality engineering practices — working closely with backend engineers (Java + Python), product managers, and platform architects. This role focuses heavily on validating hexagonal architecture adapters, Kafka event pipelines, multi-database correctness, and AI scoring model outputs.

Key Responsibilities

• Design, build, and scale automated test frameworks for Java microservices built on hexagonal architecture — with distinct test strategies for the domain core, adapter layers (MongoDB, PostgreSQL, Cosmos DB, Kafka, Elasticsearch, Redis/Aerospike), and external integrations.

• Develop and maintain comprehensive test suites: functional, integration (Testcontainers), regression, contract, and end-to-end across all adapter implementations.

• Validate Kafka event pipelines: consumer behavior, event ordering, idempotency, poison pill handling, and dead-letter queue processing. 

• Validate MongoDB document integrity, PostgreSQL relational consistency, Cosmos DB partition correctness, and Elasticsearch index accuracy across service boundaries.

• Design and execute golden dataset test scenarios — seeding realistic dealer and customer data to validate scoring accuracy, suppression correctness, and attribution logic.

• Validate Python-based AI/ML service outputs: scoring model results, recommendation correctness, and LLM response safety gates. 

• Collaborate closely with backend engineers and architects to embed quality early in hexagonal domain design and adapter implementation.

• Drive testability improvements by influencing API design, logging, observability, and error handling.

• Own release quality and go/no-go decisions by defining and tracking quality gates in CI/CD pipelines.

• Define and monitor quality metrics: test coverage, defect leakage, flakiness, MTTR, and automation ROI. 

• Lead root cause analysis for production issues and drive preventive measures.

• Mentor junior QA/SDET engineers and set best practices across teams.

Skills & Experience

• 6+ years of hands-on experience in QA Engineering, Test Automation, or SDET roles.

• Strong test automation expertise using modern Java-based frameworks; working knowledge of Python test frameworks (pytest) is a strong plus.

• Deep experience testing distributed microservices, REST APIs, and Kafka-based architectures.

• Experience with Testcontainers for integration testing across MongoDB, PostgreSQL, Kafka, and Elasticsearch locally.

• Strong understanding of Kafka testing: consumer behavior, event ordering guarantees, and idempotency validation.

• Experience with MongoDB and PostgreSQL testing patterns: data integrity, schema migration validation, multi-tenant isolation verification.

• Familiarity with Elasticsearch test strategies: index correctness, relevance, and query result validation.

• Experience with CI/CD pipeline integration (GitHub Actions, Jenkins, or equivalent).

• Familiarity with containerized environments (Docker, Kubernetes).

• Excellent problem-solving, communication, and collaboration skills.

Preferred Skills

• Experience validating AI/ML model outputs and scoring pipeline correctness.

• Exposure to contract testing (Pact or similar) and service virtualization for hexagonal adapter boundaries.

• Experience with performance, load, and chaos testing (JMeter, Gatling, k6) against Elasticsearch and Kafka.

• Background in DevOps or Site Reliability Engineering practices.

Perks & Benefits

• Competitive compensation and generous stock options. 

• Medical insurance coverage.

• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies. 

Job Description

We are seeking a Staff SDET to own the quality architecture for our AI-native platform. This is not a test execution role — it is a quality engineering leadership role. You will design and own the test strategy across all layers of the hexagonal architecture (domain core, adapters, integration, end-to-end), establish quality gates for AI model outputs, build the golden dataset evaluation framework, and set the engineering bar for Senior SDETs and QA engineers on both Scrum teams. Nothing ships without your sign-off.

Key Responsibilities

• Own the complete quality architecture: define test strategies at each hexagonal layer — domain core unit tests, adapter integration tests (Testcontainers), service-to-service contract tests (Pact), and end-to-end scenario tests.

• Design and build the golden dataset evaluation framework: seed realistic multi-tenant dealer/customer data across MongoDB, PostgreSQL, Cosmos DB, Elasticsearch, and Kafka, and define assertion criteria for scoring accuracy, suppression correctness, and attribution logic. 

• Establish AI quality gates: define evaluation criteria for ML model outputs (scoring, recommendations), LLM response quality, RAG retrieval accuracy (precision@k, recall), and agentic workflow correctness.

• Architect test infrastructure: Testcontainers configurations for MongoDB, PostgreSQL, Cosmos DB, Kafka, Elasticsearch, and Redis/Aerospike — enabling fully local integration test runs.

• Own CI/CD quality pipeline: define what automated tests run at commit, PR, staging, and pre-release gates — with blocking quality thresholds.

• Define and track quality metrics: test coverage per hexagonal layer, defect leakage, flakiness rate, MTTR, and automation ROI.

• Lead root cause analysis for production escapes — own post-mortems and preventive measure implementation. 

• Mentor Senior SDETs and QA engineers across both teams; conduct design reviews for test frameworks and golden dataset designs.

• Influence backend and frontend engineers on testability: drive API design, logging, observability, and error handling that makes services intrinsically testable. 

Skills & Experience

• 8+ years of QA Engineering, Test Automation, or SDET experience with strong hands-on engineering depth.

• Deep expertise designing test strategies for hexagonal architecture: proven ability to test domain cores independently from adapters, and adapters independently from each other.

• Strong programming skills in Java (primary) and Python — able to write production-quality test frameworks, not just test scripts. 

• Expert-level Testcontainers experience across MongoDB, PostgreSQL, Kafka, Elasticsearch, and Redis/Aerospike. 

• Experience with contract testing (Pact or similar) for service boundary validation and service virtualization.

• Deep Kafka testing expertise: consumer group behavior, event ordering guarantees, idempotency, and dead-letter queue validation.

• Experience designing golden dataset strategies: multi-tenant data seeding, realistic event sequences, and deterministic assertion design.

• Experience integrating quality gates into CI/CD pipelines with meaningful blocking thresholds.

• Strong understanding of AI/ML output validation: scoring correctness, RAG retrieval quality metrics, and agentic workflow eval design.

Preferred Skills

• Experience with performance, load, and chaos testing (Gatling, k6) against Kafka, Elasticsearch, and MongoDB at scale.

• Background in AI/ML quality engineering: eval frameworks (RAGAS or similar), model regression detection, and LLM safety testing.

• Prior experience owning quality for a platform serving multiple engineering teams (quality platform ownership).

• Familiarity with Cosmos DB testing patterns and eventual consistency validation strategies.

Perks & Benefits

• Competitive compensation and generous stock options.

• Medical insurance coverage.

• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.

As a Lead Automation QA Engineer, you will champion world-class software quality by architecting and executing advanced, hands-on automation across complex business workflows. Though you may provide strategy and mentorship, your core focus will be as a technical individual contributor running sophisticated, E2E test automation—including data, API, mobile, and cross-platform business process validations spanning the full Lead-to-Cash cycle (Salesforce, Implementation, and NetSuite). You’ll also adopt AI-based test automation, including LLM and RAG system validations.

Responsibilities

Hands-On Test Automation:

• E2E Business Process Validation:
• Design and execute automated E2E tests spanning the full Lead-to-Cash cycle, including Salesforce, downstream processes, and financial handoff to NetSuite.
• Orchestrate multi-system test scenarios to validate data flows, business process logic, integrations, and error handling across Salesforce and NetSuite.
• Develop automated test scripts to ensure synchronization and data integrity between CRM, integrated system, and ERP for business-critical workflows.
• Data Validation:
• Write and automate SQL queries for multi-source data verification, ensuring consistent, accurate information across connected systems.
• Perform automated backend checks as opportunities progress through different pipeline stages from Lead to Revenue realization.
• API & Integration Testing:
• Build and maintain robust API validation suites using Rest Assured or similar frameworks, covering all RESTful interfaces connecting Salesforce, Integrated system, and NetSuite.
• Validate integration points and data exchange between systems.
• Mobile Test Automation:
• Create and execute automated mobile tests (Appium, Espresso, XCUITest) for mobile components of business processes (if applicable).
• AI-Driven QA:
• Leverage AI-powered test automation tools and approaches to optimize and expand test coverage.
• Design and manage automated validation of Large Language Model (LLM) or Retrieval-Augmented Generation (RAG)-enabled workflows.
• DevOps Integration:
• Integrate automated E2E and component tests into CI/CD pipelines for continuous validation.

QA Leadership:

• Mentor team members in test strategy for multi-system, E2E automation.
• Review test plans and ensure adoption of QA best practices for cross-platform business process validation.
• Collaborate with cross-functional teams (Salesforce admins, implementation engineers, ERP developers) to ensure quality across business processes.

Requirements

• 8+ years’ automation QA experience, with proven E2E validation across business process systems.
• Hands-on experience automating tests across Salesforce, middleware/implementation platforms, and ERP systems like NetSuite.
• Advanced SQL query skills for backend data validation and reconciliation.
• Strong experience with API automation (Rest Assured or similar) and automated UI testing tools.
• Experience with mobile automation frameworks (Appium, Espresso, XCUITest, etc.).
• Familiarity or experience with AI/LLM/RAG or willingness to learn and implement.
• Proficient with CI/CD, Agile workflows, and test automation best practices.
• Excellent analytical, troubleshooting, and communication skills.

Job Description

We are looking for a Senior QA Engineer / SDET to drive quality across our AI-native backend services and platform systems. You will own test automation strategy, release quality gates, and quality engineering practices — working closely with backend engineers (Java + Python), product managers, and platform architects. This role focuses heavily on validating hexagonal architecture adapters, Kafka event pipelines, multi-database correctness, and AI scoring model outputs.

Key Responsibilities

• Design, build, and scale automated test frameworks for Java microservices built on hexagonal architecture — with distinct test strategies for the domain core, adapter layers (MongoDB, PostgreSQL, Cosmos DB, Kafka, Elasticsearch, Redis/Aerospike), and external integrations.

• Develop and maintain comprehensive test suites: functional, integration (Testcontainers), regression, contract, and end-to-end across all adapter implementations.

• Validate Kafka event pipelines: consumer behavior, event ordering, idempotency, poison pill handling, and dead-letter queue processing. 

• Validate MongoDB document integrity, PostgreSQL relational consistency, Cosmos DB partition correctness, and Elasticsearch index accuracy across service boundaries.

• Design and execute golden dataset test scenarios — seeding realistic dealer and customer data to validate scoring accuracy, suppression correctness, and attribution logic.

• Validate Python-based AI/ML service outputs: scoring model results, recommendation correctness, and LLM response safety gates. 

• Collaborate closely with backend engineers and architects to embed quality early in hexagonal domain design and adapter implementation.

• Drive testability improvements by influencing API design, logging, observability, and error handling.

• Own release quality and go/no-go decisions by defining and tracking quality gates in CI/CD pipelines.

• Define and monitor quality metrics: test coverage, defect leakage, flakiness, MTTR, and automation ROI. 

• Lead root cause analysis for production issues and drive preventive measures.

• Mentor junior QA/SDET engineers and set best practices across teams.

Skills & Experience

• 6+ years of hands-on experience in QA Engineering, Test Automation, or SDET roles.

• Strong test automation expertise using modern Java-based frameworks; working knowledge of Python test frameworks (pytest) is a strong plus.

• Deep experience testing distributed microservices, REST APIs, and Kafka-based architectures.

• Experience with Testcontainers for integration testing across MongoDB, PostgreSQL, Kafka, and Elasticsearch locally.

• Strong understanding of Kafka testing: consumer behavior, event ordering guarantees, and idempotency validation.

• Experience with MongoDB and PostgreSQL testing patterns: data integrity, schema migration validation, multi-tenant isolation verification.

• Familiarity with Elasticsearch test strategies: index correctness, relevance, and query result validation.

• Experience with CI/CD pipeline integration (GitHub Actions, Jenkins, or equivalent).

• Familiarity with containerized environments (Docker, Kubernetes).

• Excellent problem-solving, communication, and collaboration skills.

Preferred Skills

• Experience validating AI/ML model outputs and scoring pipeline correctness.

• Exposure to contract testing (Pact or similar) and service virtualization for hexagonal adapter boundaries.

• Experience with performance, load, and chaos testing (JMeter, Gatling, k6) against Elasticsearch and Kafka.

• Background in DevOps or Site Reliability Engineering practices.

Perks & Benefits

• Competitive compensation and generous stock options. 

• Medical insurance coverage.

• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies. 

Job Description

Key Responsibilities

• Own end-to-end delivery of CI/CD pipelines, infrastructure as code, and deployment automation workflows. 
• Collaborate with application, infrastructure, and security teams to drive automation, system reliability, and operational excellence. 
• Establish and track key metrics (KPIs) for deployment frequency, change lead time, incident reduction, and operational resilience. 
• Ensure compliance and security requirements are met through robust tooling and effective processes (e.g., secrets management, vulnerability scanning, audit logging). 
• Manage resource planning, hiring, and performance reviews; foster a collaborative and inclusive team environment. 
• Report progress, risks, and key outcomes to senior leadership; contribute to technology, roadmaps and budget planning. 

Skills and Experience

• Bachelor’s/master’s in computer science, Engineering, or related field, or equivalent practical experience. 
• 5+ years of experience in DevOps or SRE roles, including large-scale automation and modern infrastructure management. 
• Deep experience with modern DevOps tooling and practices (e.g., Kubernetes, Terraform, Jenkins, GitHub Actions, AWS/GCP/Azure). 
• Proven expertise in automation, configuration management, monitoring/alerting, and incident response. 
• Solid understanding of security best practices for cloud-native environments (e.g., IAM, RBAC, encryption, compliance frameworks). 
• Strong communication skills, with the ability to effectively manage stakeholders, prioritize work, and build high-performing teams. 

Job Description

Key Responsibilities

• Own end-to-end delivery of CI/CD pipelines, infrastructure as code, and deployment automation workflows. 
• Collaborate with application, infrastructure, and security teams to drive automation, system reliability, and operational excellence. 
• Establish and track key metrics (KPIs) for deployment frequency, change lead time, incident reduction, and operational resilience. 
• Ensure compliance and security requirements are met through robust tooling and effective processes (e.g., secrets management, vulnerability scanning, audit logging). 
• Manage resource planning, hiring, and performance reviews; foster a collaborative and inclusive team environment. 
• Report progress, risks, and key outcomes to senior leadership; contribute to technology, roadmaps and budget planning. 

Skills and Experience

• Bachelor’s/master’s in computer science, Engineering, or related field, or equivalent practical experience. 
• 5+ years of experience in DevOps or SRE roles, including large-scale automation and modern infrastructure management. 
• Deep experience with modern DevOps tooling and practices (e.g., Kubernetes, Terraform, Jenkins, GitHub Actions, AWS/GCP/Azure). 
• Proven expertise in automation, configuration management, monitoring/alerting, and incident response. 
• Solid understanding of security best practices for cloud-native environments (e.g., IAM, RBAC, encryption, compliance frameworks). 
• Strong communication skills, with the ability to effectively manage stakeholders, prioritize work, and build high-performing teams.