GEN AI

Experience Required 11+ years in AI/ML development, with 3+ years specialized in Generative AI and LLM applications. Role Overview The AI Lead Engineer will design, build, and operate production-grade Generative AI solutions for complex enterprise scenarios. The role focuses on scalable LLM-powered applications, robust RAG pipelines, and multi-agent systems with MCP deployed across major cloud AI platforms. Key Responsibilities Technical Leadership & Development • Design and implement enterprise-grade GenAI solutions using LLMs (GPT, Claude, Llama and similar families). • Build and optimize production-ready RAG pipelines including chunking, embeddings, retrieval tuning, query rewriting, and prompt optimization. • Develop single- and multi-agent systems using LangChain, LangGraph, LlamaIndex and similar orchestration frameworks. • Design agentic systems with robust tool calling, memory management, and reasoning patterns. • Author MCP (Model Context Protocol) servers, tools, and resources, and integrate them with Cursor, Claude, Codex, Copilot, and internal enterprise systems. • Build plugins and extensions for Claude, Codex, Cursor and GitHub Copilot ecosystems. • Building AI Agents and Sub-Agents, Agent Skills for tools like Claude Code, Codex, and GitHub Copilot. • Build scalable Python + FastAPI/Flask or MCP microservices for AI-powered applications, including integration with enterprise APIs. • Implement model evaluation frameworks using RAGAS, DeepEval, or custom metrics aligned to business KPIs. • Implement agent-based memory management using Mem0, LangMem or similar libraries. • Fine-tune and evaluate LLMs for specific domains and business use cases. • Deploy and manage AI solutions on Azure (Azure OpenAI, Azure AI Studio, Copilot Studio), AWS (Bedrock, SageMaker, Comprehend, Lex), and GCP (Vertex AI, Generative AI Studio). • Implement observability, logging, and telemetry for AI systems to ensure traceability and performance monitoring. • Ensure scalability, reliability, security, and cost-efficiency of production AI applications. • Deep understanding of RAG architectures, hybrid retrieval, and context engineering patterns. • Translate business requirements into robust technical designs, architectures, and implementation roadmaps. • Drive innovation by evaluating new LLMs, orchestration frameworks, and cloud AI capabilities (including Copilot Studio for copilots and workflow automation). Required Skills & Experience Core Technical • Programming: Expert-level Python with production-quality code, testing, and performance tuning. • GenAI Frameworks: Strong hands-on experience with LangChain, LangGraph, LlamaIndex, agentic orchestration libraries. • LLM Integration: Practical experience integrating OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, and Vertex AI models via APIs/SDKs. • RAG & Search: Deep experience designing and operating RAG workflows (document ingestion, embeddings, retrieval optimization, query rewriting). • Vector Databases: Production experience with at least two of OpenSearch, Pinecone, Qdrant, Weaviate, pgvector, FAISS. Cloud & AI Services • Azure: Azure OpenAI, Azure AI Studio, Copilot Studio, Azure Cognitive Search. • AWS: Bedrock, SageMaker endpoints, AWS Nova, AWS Transform etc. • GCP: Vertex AI (models, endpoints), Agentspace, Agent Builder. Preferred Qualifications • Master's degree in Computer Science, AI/ML, Data Science, or related field. • Experience with multi-agent systems, Agent-to-Agent (A2A) communication, and MCP-based ecosystems. • Familiarity with LLMOps / observability platforms such as LangSmith, Opik, Azure AI Foundry. • Experience integrating graph databases and knowledge graphs to enhance retrieval and reasoning.