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.