Generative AI Engineer

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 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).