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.
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 Google Cloud Platform (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).
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.
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