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 Overview
We are seeking a senior, technically strong Product Manager to own platform capabilities across one of our engineering Scrum teams while driving customer-facing outcomes and continuous improvement. This role requires close collaboration with Engineering, AI, and platform teams, along with hands-on understanding of event-driven systems, agentic AI integration, scalable service design, and data-driven product development.

Key Responsibilities
• Own the product backlog for a Scrum team — writing clear, actionable user stories, acceptance criteria, and feature specifications aligned with business goals.
• Partner closely with Engineering and AI teams to deliver high-quality solutions across Java microservices, Kafka event pipelines, and agentic platform integrations.
• Collaborate with platform and infrastructure teams to ensure seamless integrations, well-defined data contracts, and API alignment.
• Track success metrics, adoption, and outcomes — iterating rapidly based on data and field feedback.
• Author Intent definitions, Skill specifications, and Policy configurations for the agentic platform layer (PRDas-Code approach).
• Design and maintain golden dataset scenarios used to validate intelligent capabilities before release.
• Drive change management and adoption of new features, tools, and platform capabilities.
• Build and maintain automations using workflow tools (Zapier or equivalent) to streamline internal processes.
• Demonstrate strong understanding of RESTful APIs, webhooks, and authentication mechanisms; troubleshoot integration issues hands-on.
• Work with data teams to define schemas, reporting views, and analytics instrumentation.
• Facilitate prioritization discussions, manage stakeholder expectations, and communicate trade-offs clearly across Engineering, Design, and Leadership.

Skills & Qualifications
• 6+ years of Product Management experience, preferably in platform, data-driven, or AI-enabled products.
• Strong experience collaborating with engineering teams on technical products — comfortable reading system designs, API specs, and data models.
• Proven analytical and problem-solving skills with a data-driven mindset; experience defining and tracking product KPIs.
• Hands-on experience with RESTful APIs, webhooks, and system integrations (ERP, CRM, data platforms, or cloud services).
• Familiarity with AI systems, LLM-based tools, and agentic AI patterns (intent routing, skill/policy design, MCP tools).
• Experience designing scalable, maintainable solution architectures in collaboration with engineering.
• Excellent verbal and written communication skills; strong stakeholder management across technical and non-technical audiences.
• Structured problem-solving and critical thinking; ability to navigate complex system trade-offs.
• Bachelor's or Master's degree in IT, Computer Science, Business Administration, or a related field.

Good to Have
• Experience authoring PRD-as-Code artifacts: Intent definitions, Skill specs, Policy configurations.
• Background in marketing technology, CDP, or customer data platforms.
• Experience with LMS platforms and user enablement programs.
• Prior experience in automotive retail or dealer technology.

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
• 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.

Summary:
Tekion seeks an exceptionally talented and seasoned Senior Staff Applied Scientist to architect the future of our AI-driven solutions. The incumbent will assume a pivotal role in our organization, providing the strategic and technical stewardship necessary to translate cutting-edge research into tangible business value. The ideal candidate is a first-principles thinker, adept at navigating ambiguity, and possesses the intellectual curiosity to formulate and solve complex, unstructured problems. This is a seminal opportunity to influence the trajectory of our AI strategy and deliver transformative solutions at an enterprise scale. 

Key Responsibilities:

• Strategic Direction: Architect and execute the strategic vision for applied AI and Machine Learning within Tekion, ensuring rigorous alignment with overarching business objectives and industry challenges.
• Innovation & Research Leadership: Steer the R&D roadmap by identifying and capitalizing on emergent technological opportunities. Drive innovation through the ideation and implementation of novel, scientifically-grounded solutions.
• Stakeholder Alignment: Serve as the principal technical liaison between the R&D organization and executive stakeholders, ensuring technical strategies are congruent with business imperatives.
• Cross-Functional Execution: Lead collaborations with product, engineering, and business units to define, develop, and integrate sophisticated machine learning systems into core products and services.
• Scientific Contribution: Contribute to the broader scientific community through publications in premier academic conferences and journals, enhancing Tekion's position as a center of innovation.
• Mentorship & Talent Development: Mentor and cultivate a culture of scientific rigor and innovation among applied scientists, guiding project execution and fostering professional growth.
• Impact Quantification: Establish robust frameworks for model evaluation and performance measurement. Quantify and articulate the business impact of machine learning initiatives to executive leadership.
• Governance & Best Practices: Institute and govern best practices for the entire machine learning lifecycle, including model development, validation, deployment, and monitoring, while championing ethical and responsible AI

Core Competencies & Technical Expertise
The successful candidate will demonstrate mastery in the following areas:

• Foundational Expertise: Deep, theoretical and practical expertise in Machine Learning, Deep Learning, Causal Inference, and Explainable AI.
• Statistical Rigor: Advanced proficiency in applied probability and statistics to derive and validate insights from complex, high-dimensional data.
• Deep Learning:
  • Expert-level proficiency with frameworks such as TensorFlow, Keras, and PyTorch.
  • Extensive experience implementing advanced neural network architectures.
  • Practical application of Computer Vision (e.g., OpenCV) and Natural Language Processing (e.g., spaCy) methodologies.
• Large Language Models (LLMs): Demonstrated experience with Large Language Models, including advanced prompt engineering, fine-tuning, and deployment for specific business applications.
• Technical Proficiencies:
  • Advanced programming skills in Python and mastery of SQL. Familiarity with distributed computing frameworks (e.g., Spark) is advantageous.
  • Proficiency with cloud computing platforms (GCP, Azure, AWS).
  • Expertise in experimental design (A/B testing, causal inference).
  • Proficient in version control systems (Git).

Required Qualifications

• An advanced degree (Ph.D. or M.S. preferred) in a quantitative discipline such as Computer Science, Statistics, Operations Research, Physics, or a related field.
• A minimum of 10+ years of post-academic experience in applied science or quantitative research roles, with a demonstrable history of translating complex models into tangible business value.
• A distinguished track record of solving difficult, business-critical problems using a scientific, data-driven approach.
• Proven experience in programming, data manipulation, and building models in a business context.
• Sophisticated data visualization and presentation skills, with the ability to distill complex analytical results into clear, actionable insights for diverse audiences.

Ideal Candidate Profile

• A profound intellectual curiosity and a systematic, first-principles approach to problem-solving.
• Domain expertise in the automotive industry or related fields such as finance or enterprise SaaS is highly desirable.
• Experience in specialized areas such as Recommendation Engines or advanced NLP is preferred.
• Exceptional communication and presentation capabilities, with a proven ability to distill complex technical concepts for executive-level audiences.
• A demonstrated ability to work collaboratively and effectively lead initiatives across multi-disciplinary teams.
• A commitment to continuous personal and team development within a collaborative, high-performance culture.

Address:

Bangalore Office  

Tekion India Private Limited 

6th, 7th & 8th Floor, Helium, Bagmane Solarium City, Doddanekkundi Village, KR Puram Hobli, East Taluk, Bangalore - 560037 

Chennai Office

Embassy Splendid Zone, Block 2, 9th floor, Velan Nagar, Arulmurugan Nagar, Old Pallavaram, Chennai, Tamil Nadu 600043

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 Principal Applied Scientist to be the highest-level AI technical authority on the team. This role is above the Applied Scientist level — you set the AI research and engineering direction, own the agentic system architecture, define the eval framework standards, and operate as the primary interface to the external AI research community and internal platform AI leadership. You will combine deep research rigor with relentless production focus: every AI capability you design ships, gets measured, and improves based on evidence.

You are the person who answers: "Is this AI approach the right one, and how do we know when it's working"

Key Responsibilities

• Own the agentic system architecture: define the multi-agent coordination patterns, MCP tool taxonomy, intent-to-skill routing logic, policy enforcement design, and memory management strategy across all AI workflows on the platform.
• Lead the AI evaluation framework: design the golden dataset structure, define eval metrics (RAG retrieval quality, scoring accuracy, LLM response correctness, agentic workflow success rate), and own the CI/CD AI eval pipeline that gates every AI capability release.
• Drive the RAG architecture strategy: chunking strategies for MongoDB and Elasticsearch-indexed documents, embedding model selection and fine-tuning, retrieval reranking design, and hallucination mitigation patterns.
• Own the LLM integration architecture: prompt engineering standards, LLM gateway usage policies, context window management, streaming response patterns, and model version governance.
• Lead applied research initiatives: identify novel techniques (RLHF, Constitutional AI, multi-modal, sparse retrieval) that are practically applicable to automotive retail intelligence use cases and prototype them rigorously in Python.
• Define the hexagonal AI adapter contract standard: the formal interface specification between Python AI services and Java domain cores — ensuring AI infrastructure remains swappable without domain logic changes.
• Act as primary technical interface to platform AI leadership (LLM Gateway, ML Platform, Agentic Engine) — influencing platform roadmap based on teams practical requirements.
• Mentor the Applied Scientist and ML Architect; conduct technical reviews of AI system designs, eval frameworks, and research approaches.
• Publish, present, or contribute to open-source where appropriate — representing Tekion's AI engineering excellence externally.

Skills & Qualifications

• 10+ years of applied AI/ML experience with at least 3 years at Staff or Principal level — a track record of shipping AI systems that measurably changed business outcomes.
• Deep expertise across: LLM fine-tuning and alignment, RAG system design (chunking, embedding, retrieval, reranking), agentic frameworks (LangGraph, CrewAI, AutoGen), and MCP tool design.
• Python mastery: PyTorch, Hugging Face Transformers, LangChain/LangGraph, scikit-learn, and production ML serving tooling.
• Strong understanding of distributed AI systems: Elasticsearch-backed vector search, Redis/Aerospike serving, Cosmos DB for AI event logging, and Kafka-based feature pipelines.
• Proven ability to design formal eval frameworks for AI systems — not just accuracy metrics but business aligned quality gates.
• Experience defining and enforcing hexagonal adapter contracts between AI services and domain service layers in production systems.
• Track record of communicating complex AI system design to non-technical stakeholders, influencing product roadmaps, and driving cross-team alignment. 

Good to Have

• Publications or significant open-source contributions in NLP, information retrieval, or agentic systems.
• Experience with Constitutional AI, RLAIF, or preference optimization techniques (DPO, PPO).
• Familiarity with multi-modal AI and its application to automotive retail data (images, documents, tabular data fusion).
• Prior experience in a product-focused AI role at a SaaS or platform company — not purely research. 

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.

Summary:

Leads advanced financial analysis and optimization strategies for cloud infrastructure, providing expert-level insight and technical guidance to drive cost efficiency, forecasting accuracy, and financial accountability across multi-cloud environments. This role partners closely with engineering, finance, and procurement teams to enable strategic cloud investment decisions.​

Duties & Responsibilities:

• ​ Lead in-depth cost modeling, forecasting, and trend analysis for AWS, Azure, GCP, and other cloud platforms.
• Design and implement scalable chargeback/showback models, allocation frameworks, and budget controls across business units.
• Serve as the primary expert on cloud cost optimization, including automation of rightsizing, usage analysis, and commitment planning (RI/SP/CUD).
• Collaborate with cloud engineering and architecture teams to ensure cost considerations are embedded in system design and scaling strategies.
• Provide financial oversight and scenario modeling to support large-scale cloud investments and vendor negotiations.
• Own and maintain FinOps dashboards, data pipelines, and cloud billing integrations with BI platforms.
• Define and drive FinOps best practices, governance standards, and cross-functional training initiatives.
• Mentor other analysts and serve as a lead contributor to FinOps maturity and tooling strategies.​

Job Complexity:

Leads the most complex, high-impact cloud financial analysis and optimization initiatives. Translates technical usage into strategic financial insights.

Required Supervision:

Operates independently with a high degree of autonomy. Trusted to guide FinOps execution and influence enterprise cloud strategy.

Expertise:

Deep subject matter expertise in cloud billing, cost modeling, and FinOps strategy. Recognized as a key influencer in cloud financial planning and optimization.​

Qualifications:

• ​ Bachelor’s or Master’s degree in Finance, Data Science, Engineering, or a related field.
• 7+ years of experience in cloud cost optimization, infrastructure finance, or enterprise technology operations.
• Advanced knowledge of public cloud platforms (AWS, Azure, GCP), billing constructs, and pricing strategies.
• Proficiency in SQL, Excel, and BI tools such as Tableau, Power BI, or Looker.
• Experience with cloud cost management platforms (e.g., Apptio Cloudability, CloudHealth, native tools).
• Strong technical acumen, communication skills, and ability to collaborate across engineering and finance.
• FinOps Certified Practitioner or Professional strongly preferred.

Summary:

Leads advanced financial analysis and optimization strategies for cloud infrastructure, providing expert-level insight and technical guidance to drive cost efficiency, forecasting accuracy, and financial accountability across multi-cloud environments. This role partners closely with engineering, finance, and procurement teams to enable strategic cloud investment decisions.​

Duties & Responsibilities:

• ​ Lead in-depth cost modeling, forecasting, and trend analysis for AWS, Azure, GCP, and other cloud platforms.
• Design and implement scalable chargeback/showback models, allocation frameworks, and budget controls across business units.
• Serve as the primary expert on cloud cost optimization, including automation of rightsizing, usage analysis, and commitment planning (RI/SP/CUD).
• Collaborate with cloud engineering and architecture teams to ensure cost considerations are embedded in system design and scaling strategies.
• Provide financial oversight and scenario modeling to support large-scale cloud investments and vendor negotiations.
• Own and maintain FinOps dashboards, data pipelines, and cloud billing integrations with BI platforms.
• Define and drive FinOps best practices, governance standards, and cross-functional training initiatives.
• Mentor other analysts and serve as a lead contributor to FinOps maturity and tooling strategies.​

Job Complexity:

Leads the most complex, high-impact cloud financial analysis and optimization initiatives. Translates technical usage into strategic financial insights.

Required Supervision:

Operates independently with a high degree of autonomy. Trusted to guide FinOps execution and influence enterprise cloud strategy.

Expertise:

Deep subject matter expertise in cloud billing, cost modeling, and FinOps strategy. Recognized as a key influencer in cloud financial planning and optimization.​

Qualifications:

• ​ Bachelor’s or Master’s degree in Finance, Data Science, Engineering, or a related field.
• 7+ years of experience in cloud cost optimization, infrastructure finance, or enterprise technology operations.
• Advanced knowledge of public cloud platforms (AWS, Azure, GCP), billing constructs, and pricing strategies.
• Proficiency in SQL, Excel, and BI tools such as Tableau, Power BI, or Looker.
• Experience with cloud cost management platforms (e.g., Apptio Cloudability, CloudHealth, native tools).
• Strong technical acumen, communication skills, and ability to collaborate across engineering and finance.
• FinOps Certified Practitioner or Professional strongly preferred.

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 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.

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. 

Role Overview

This is an individual contributor role requiring strong product instincts, technical fluency, and the ability to operate autonomously across the full delivery lifecycle. The ideal candidate is equally comfortable in a whiteboard session defining a solution and in a sprint review tracking execution details Expectations Own the intake, prioritization, and end-to-end product delivery of AEC platform requirements driven by OEM program teams. This role works closely with AEC platform product and engineering stakeholders to ensure solutions are scalable and reusable, while partnering with program teams to translate OEM needs into clear requirements and execution-ready work items.

Key Responsibilities

· Consolidate and prioritize platform asks across OEM programs with program leadership; manage tradeoffs and release scope

· Define solutions and author high-quality PRDs covering scope, workflows, data contracts, acceptance criteria, and rollout; create and manage epics and stories in the tracking system

· Drive cross-team solution alignment; identify dependencies early and secure commitments

· Lead grooming with engineering, QA, and TPMs to obtain estimates, sequencing, and risk/dependency tracking; ensure release readiness

· Track execution through release, escalate blockers, and maintain clear stakeholder communication

· Post-release: monitor outcomes, triage issues versus enhancements, and feed learnings back into the roadmap

Ideal Candidate

 Must-haves: 5+ years in enterprise SaaS PM, strong PRD writing skills, executive stakeholder management, platform architecture fluency, and experience leading engineering grooming independently.

Strong plus: Automotive retail or DMS experience, familiarity with OEM-dealer dynamics, exposure to Tekion AEC or comparable platforms. 

Key Hire Signal
This person must be able to design solutions and write PRDs that survive expert review without needing platform PMs in the room. If they rely on collaborative grooming to refine requirements, they will create bottlenecks in the delivery cycle. 

Perks and Benefits

• Competitive compensation

• Generous stock options

• Medical Insurance coverage

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