Senior AI/ML Engineer

Role: Senior AI/ML Engineer GenAI / LLM / Agentic AI

Location: Whitefield, Bangalore
Work Mode: Hybrid 3 days/week from office
Experience: 6+ years overall, including 3+ years in production-grade LLM/GenAI engineering

About the Role

We are looking for a Senior AI/ML Engineer to join our AI Platform team and build production-grade AI agents for financial close automation.

This is a hands-on engineering role for someone who has moved beyond LLM demos and experimentation and has experience designing, building, deploying, and monitoring reliable LLM/agentic AI systems in production.

You will work with a cross-functional engineering squad and own the LLM and agent layer, including agent orchestration, RAG, LLM integrations, evaluation, observability, guardrails, and production deployment.

What You'll Work On

• Design and build multi-step AI agent workflows using LangChain and LangGraph
• Implement agent tools, state management, routing, handoffs, and human-in-the-loop workflows
• Integrate Azure OpenAI and other frontier LLMs such as Claude, Gemini, or Mistral
• Work with structured outputs, function/tool calling, prompt engineering, token optimization, and latency optimization
• Build production-grade RAG pipelines covering document ingestion, embeddings, retrieval, and generation
• Work with vector databases such as pgvector, Pinecone, Qdrant, Weaviate, or FAISS
• Implement LLM observability and tracing using Langfuse or equivalent platforms
• Build evaluation frameworks and regression testing for prompts, models, and RAG pipelines
• Implement guardrails, confidence thresholds, fallbacks, and human escalation mechanisms
• Ensure agent decisions and tool calls are traceable and auditable
• Build AI/agent service APIs using Python and FastAPI
• Work with PostgreSQL and agent state/audit data
• Containerize and deploy services using Docker and Kubernetes
• Work with Azure DevOps CI/CD for automated build and deployment
• Collaborate with architects and software engineers on agent orchestration and communication patterns

Must-Have Skills

• 6+ years of overall software engineering experience
• 3+ years of hands-on experience building and deploying production LLM/GenAI applications
• Strong hands-on experience with LangChain and LangGraph
• Experience building production-grade AI agents/agentic workflows
• Strong Python development experience
• Experience with Azure OpenAI or other LLM APIs
• Hands-on experience with RAG
• Experience with at least one vector database: pgvector / Pinecone / Qdrant / Weaviate / FAISS
• Experience with LLM observability/evaluation tools such as Langfuse
• Understanding of prompt versioning, evaluation, regression testing, and model/prompt lifecycle management
• Experience with guardrails, fallback handling, confidence thresholds, and traceability
• Experience with FastAPI or equivalent Python async frameworks
• Experience with PostgreSQL
• Experience with Docker and Kubernetes
• Experience with CI/CD, preferably Azure DevOps

Good to Have

• Hands-on exposure to MCP (Model Context Protocol) and/or A2A (Agent-to-Agent)
• Experience with Google ADK, AutoGen, CrewAI, or Semantic Kernel
• Experience in financial/accounting applications
• Understanding of SOX/GDPR requirements for AI auditability
• Experience with LLM fine-tuning or federated learning
• Open-source contributions to AI/LLM/agent frameworks

Important

This position is not focused on traditional ML research or LLM experimentation/POCs.

We are specifically looking for engineers who have built and shipped reliable, observable, and production-ready LLM/agent systems.