Job highlights

- Graduate/Postgraduate in CS/Engineering with 6+ years experience, expertise in LangChain, LangGraph, MCP, RAG architecture, vector databases, and Python

- Design and implement multi-agent AI systems, build RAG pipelines, integrate LLMs with enterprise systems, deploy AI solutions on Azure using AKS and containerization, and collaborate with product and data teams

Primary Responsibilities

- Design and implement multi-agent AI systems using LangChain, LangGraph, MCP, and advanced memory architectures

- Build Retrieval-Augmented Generation (RAG) pipelines including text chunking, embedding, indexing, and vector retrieval

- Develop and maintain AI agent orchestration frameworks, ensuring reliable tool use, memory updates, and agent to agent communication

- Integrate LLMs with enterprise systems through secure API patterns, prompt orchestration, and contextual memory

- Implement and optimize vector database solutions

- Architect production-reference AI pipelines on Azure using:

- Azure Foundry (Azure AI Foundry) for model hosting, evaluation, and monitoring

- Azure AI Search for hybrid retrieval, semantic search, and grounding

- Azure Container Apps / Azure Kubernetes Service (AKS) for scalable deployment

- Containerize and deploy AI systems using Docker,



Azure Container Registry (ACR), and orchestrate using ACA/AKS

- Productionize agentic/RAG solutions with CI/CD, observability, autoscaling, and reliability best practices

- Collaborate with product and data engineering teams to deliver end-to-end AI capabilities

- Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and versatile work arrangements

Required Qualifications

- Graduate or Postgraduate in Computer Science /Engineering/Science or related field, B.Tech./MCA/Graduation (15+ years of formal education, Correspondence courses are not relevant).

- 6+ years of total experience

- Hands-on expertise with Lang Chain, Lang Graph and MCP (Model Context Protocol).

- Experience with RAG architecture, including Vector storage and retrieval, Embedding pipelines, Query routing and relevance tuning.

- Practical knowledge of vector databases and similarity search.

- Solid understanding of AI concepts, including multi-agent systems, Memory architectures, Reasoning & tool driven agents.

- Proficient in Python, AI/ML libraries, and API development.

📌 AI or ML Engineer (Hyderabad)
🏢 Optum
📍 Hyderabad