RESPONSIBILITIES
Design architecture for RAG systems, agentic workflows and LLM integrations
Define standards for AI evaluation, model selection, prompt engineering and production readiness
Review technical designs produced by AI Engineers before implementation
Own the AI platform roadmap and communicate it to product and leadership teams
Hands-on implementation on high-complexity features — not purely supervisory
Mentor AI Engineers and Backend Engineers on AI-specific patterns
Maintain awareness of model capabilities, API updates and relevant open-source developments

REQUIRED SKILLS
Python — production-grade development and code review
LLM API integration — OpenAI, Anthropic or Azure OpenAI. Streaming, function calling, structured outputs, error handling
RAG architecture — has designed and shipped retrieval-augmented generation systems. Can articulate choices around chunking, embedding models, reranking and evaluation




Agentic systems — has built multi-step agent pipelines with tool use, memory and error recovery
LLM evaluation — automated test frameworks, hallucination detection, retrieval quality measurement
Vector databases — Milvus, Pinecone, Weaviate, Chroma or pgvector in production
FastAPI or equivalent — API design and review
Docker, cloud (AWS/Azure/GCP), basic Kubernetes — can review and guide deployment decisions
Data Systems – Hands-on with Databases, Data architecture and distributed data systems
PREFERRED BACKGROUND
10+ years of software engineering with at least 2 years on AI or ML systems
Has led AI delivery teams, shipped at least one RAG system or agentic workflow in a production setting
Experience in professional services, financial services or enterprise SaaS is an advantage
Fine-tuning experience (LoRA/QLoRA) is a plus, not a requirement

📌 AI Transformation Lead (Mumbai)
🏢 BDO India
📍 Mumbai