Principal, Applied Science at Tekion

Chennai, tamil nadu, India -

Full Time

Start Date

Immediate

Expiry Date

07 Jun, 26

Salary

0.0

Posted On

09 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

LLM Fine-Tuning, RAG System Design, Agentic Frameworks, Python, PyTorch, Hugging Face Transformers, LangGraph, Scikit-learn, Distributed AI Systems, Vector Search, Evaluation Frameworks, Hexagonal Adapter Contracts, Prompt Engineering, RLHF, Multi-modal AI, System Architecture

Industry

Software Development

Description

About Tekion: Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe. 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 TAMC's 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. Tekion is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, victim of violence or having a family member who is a victim of violence, the intersectionality of two or more protected categories, or other applicable legally protected characteristics. For more information on our privacy practices, please refer to our Applicant Privacy Notice here.

Responsibilities

This role serves as the highest-level AI technical authority, responsible for setting the AI research and engineering direction, owning the agentic system architecture, and defining evaluation framework standards. Key duties include owning the architecture for multi-agent coordination, leading the AI evaluation framework, driving RAG and LLM integration strategies, and leading applied research initiatives.