GenAI / Agentic AI Engineer (LLM, LangGraph, RAG)

Key Responsibilities

• Design, develop, and deploy AI-powered applications using LLMs, Agentic AI frameworks, and modern AI architectures.
• Build and orchestrate multi-agent systems using LangGraph, A2A, MCP, and related frameworks.
• Design and implement scalable RAG pipelines using vector databases, embeddings, and semantic search.
• Develop REST APIs and AI services using Python, FastAPI, and Pydantic.
• Integrate AI solutions with AWS, Azure, or GCP cloud services.
• Build and optimize prompt engineering, tool-calling, and agent workflows.
• Develop LLM evaluation, monitoring, and optimization frameworks.
• Create AI-driven user experiences and business workflows powered by intelligent agents.
• Collaborate with business, compliance, IT, and data teams to deliver enterprise AI solutions.
• Leverage AI-assisted development practices for code generation, testing, documentation, and refactoring.
• Implement cloud-native deployment strategies using Docker, CI/CD, and modern DevOps practices.
• Stay updated on emerging GenAI, Agentic AI, and LLM technologies.

Required Skills

• 8+ years of Software Engineering or Data Science experience.
• Minimum 2+ years of hands-on experience in Generative AI, LLMs, or Agentic AI solutions.
• Strong expertise in Python development.
• Experience building AI Agents using LangGraph and related agent frameworks.
• Strong knowledge of LLMs, embeddings, prompt engineering, and tool integrations.
• Hands-on experience with Retrieval-Augmented Generation (RAG) architectures.
• Experience developing APIs using FastAPI and Pydantic.
• Experience with MCP (Model Context Protocol) and A2A integrations.
• Knowledge of cloud platforms such as AWS, Azure, or GCP.
• Experience with Docker, CI/CD pipelines, and cloud-native architectures.

Preferred Skills

• Insurance or Annuities domain experience.
• Experience with Azure OpenAI, AWS Bedrock, Vertex AI, or similar AI services.
• Vector databases such as Pinecone, Weaviate, Chroma, or OpenSearch.
• AI observability, evaluation, and governance frameworks.
• Knowledge of MLOps and AI platform engineering.
• Experience building production-grade AI applications.