Job Description
AI/ML Tech LeadExperience Level: 6+ Years
Location: Chennai, Hybrid
Full Time
Role Overview
We are seeking a high-ownership, hands-on AI / LLM Engineering Tech Lead to drive the technical execution of our core AI platforms. In this role, you will bridge deep architectural vision with direct code-level execution. You will lead an agile engineering pod building enterprise-grade Agentic Workflows, Knowledge Graphs, and AI Security Safeguards.
If you are driven by passion and innovation, determined to bend the limits to build something truly transformative — this role is for you.
Key Responsibilities
● Architectural Leadership & Pod Execution
○ Lead an engineering pod (AI/ML Engineers, Full-Stack Developers, and DevOps) to ship low-latency, production-ready AI features.
○ Translate high-level blueprints into actionable technical specifications, clean codebases, and sprint backlogs.
○ Enforce engineering excellence through code reviews, automated CI/CD testing protocols, and robust error-handling standards.
● Hands-On Agentic & Knowledge Systems Development
○ Architect & Code: Build multi-modal LLM workflows and autonomous agentic systems using modern orchestration frameworks.
○ Knowledge Layer Integration: Implement knowledge graphs, dynamic ontologies, and advanced vector retrieval strategies (Hybrid Search, Graph RAG, Re-ranking) that go beyond standard naive RAG.
○ AI Security & Guardrails: Deploy active safeguards against prompt injection, model jailbreaks, hallucination, and data leakage using core AI Security principles.
● Production MLOps, Eval & Performance
○ LLM Ops: Build automated pipelines for continuous model evaluation (e.g. RAGAS, TruLens), dynamic prompt versioning, and latency tracking.
○ Cost & Throughput Optimization: Optimize token consumption, context window management, caching, and model inference costs across multi-cloud deployments.
○ Observability: Monitor model drift, data distribution shifts, and edge-case execution in live enterprise production environments.
● Cross-Functional Execution
○ Collaborate closely with Product Managers, Solution Architects, and client teams to resolve complex edge cases and accelerate feature delivery.
○ Serve as a technical mentor, elevating team execution standards and unblocking complex algorithmic or system challenges daily.
Required Qualifications
● Education & Experience:
○ Experience: 5+ years of core software engineering experience, including 3+ years specifically architecting and delivering AI/ML or LLM-based products into production.
○ Leadership: Proven track record leading agile pods, conducting technical design
reviews, and mentoring developers.
○ Education: Master’s in Computer Science, Data Science, AI, or equivalent practical experience demonstrated through shipped products or open-source contributions and professional certifications.
● Technical Stack Requirements:
○ Languages & Core CS: Strong mastery of Python (FastAPI, PyDantic, Asyncio) with familiarity in TypeScript, Go, or Java.
○ Agentic Frameworks & AI Stack: Hands-on experience with modern LLM orchestration tools (LangGraph, AutoGen, CrewAI, LangChain, LlamaIndex), PyTorch, Hugging Face, and major LLM Provider APIs.
○ Vector Engines & Knowledge Graphs: Direct working experience with vector databases (Qdrant, Pinecone, Milvus, Weaviate) and Knowledge Graph technologies (Neo4j, RDF/Ontologies).
○ AI Security & Guardrails: Familiarity with adversarial prompt testing, red teaming concepts, and guardrail implementation.
○ MLOps & Infra: Practical experience with Docker, Kubernetes, GitHub Actions, MLflow, Weights & Biases, and serverless AI infrastructure on AWS/GCP/Azure.
● Soft Skills:
○ Strong technical articulation and communication skills to engage with technical stakeholders, understand requirements, and present engineering solutions cleanly.