The Lead AI Engineer is the technical anchor for the company's AI Engineering & Operations team. This role owns the architecture, design patterns, and technical direction for all AI agent development — from RAG pipelines and MCP integrations to evaluation frameworks and production deployment. The Lead AI Engineer builds production AI systems, mentors a growing team of AI engineers, and translates the AI program strategy into shipped, working systems.

This is a hands-on leadership role. You write code, review code, and set the patterns that the rest of the team follows.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal prospect/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

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Skills and Requirements

Must Have

- Languages: Python (primary),



SQL
- AI/ML Frameworks: LangChain or LangGraph, Hugging Face Transformers, PyTorch or TensorFlow
- LLM Experience: Production experience with Anthropic Claude API, OpenAI API, or Azure OpenAI — including prompt engineering, chain-of-thought design, and system prompt architecture
- RAG: End-to-end RAG pipeline experience — embedding models, vector databases (Pinecone, Weaviate, pgvector, FAISS), retrieval optimization
- Agentic Systems: Experience building multi-step agent workflows with tool use in production
- Evaluation: Experience designing and running evaluation pipelines for AI/ML systems
- MLOps: MLflow, experiment tracking, model versioning, CI/CD for ML
- Cloud: Azure AI Studio/Foundry, or AWS Bedrock, or GCP Vertex AI
- Infrastructure: Docker, Kubernetes basics, REST API design
- Version Control: Git, code review practices, CI/CD pipelines
- Leadership: 2 years leading or mentoring engineering teams

Strong Preference

- MCP (Model Context Protocol) or equivalent tool-use framework experience
- Fine-tuning and RLHF experience
- LLM observability tools (LangSmith, Arize, Helicone, Weights & Biases)
- TypeScript/JavaScript for full-stack agent interfaces
- Experience with guardrail frameworks (Guardrails AI, NeMo Guardrails)
- Azure AI certifications (AI-102 or Azure AI Apps and Agents Developer Associate) Nice to Have
- MS or PhD in AI/ML, Computer Science, or related field
- Manufacturing or industrial sector experience
- Experience with computer vision (OpenCV, YOLO)
- AWS Certified Generative AI Developer Professional
- Google Professional ML Engineer certification

📌 INTL AI Engineer (Chennai)
🏢 Insight Global
📍 Chennai