Gen AI Developer Lead

Role Overview

Technical and project lead responsible for driving the architecture, development, and deployment of production-grade GenAI systems, multi-agent workflows, and enterprise AI adoption.

Key Responsibilities

• Technical Leadership: Design and deploy production-grade AI agents, multi-agent systems, and scalable distributed AI architectures.

• AI Engineering: Build RAG pipelines, LangGraph workflows, MCP/A2A integrations, and context/prompt engineering strategies.

• Quality & Governance: Implement AI evaluation frameworks (hallucinations, latency, quality) and establish responsible AI, security, and governance standards.

• Project & Stakeholder Management: Own roadmaps, delivery plans, and cross-functional communication with product, security, and leadership teams.

Must-Have Requirements

• Experience: 8+ years in software engineering, including 3+ years in GenAI/LLM applications and 3+ years leading technical teams/large initiatives.

• Languages: Strong Java and Python (Mandatory).

• AI Frameworks & Concepts: LangChain, LangGraph, AI Agents/A2A, Model Context Protocol (MCP), Prompt/Context Engineering, and RAG architectures.

• Databases & Evaluation: Vector DBs (Pinecone, FAISS, Weaviate, or OpenSearch) and AI evaluation frameworks.

• Cloud & DevOps: Azure Cloud, Azure OpenAI (or OpenAI APIs), Kubernetes, Docker, Git, and CI/CD.

Nice-to-Have Skills

• Frameworks: LlamaIndex, CrewAI, AutoGen, Semantic Kernel, FastMCP, FastAPI, Node.js, or Angular.

• Advanced AI: GraphRAG, Knowledge Graphs, and evaluation tools (Ragas, DeepEval).

• Infrastructure: Event-driven architecture (Kafka, Redis, RabbitMQ) and OpenSearch hybrid search.