AI Lead Engineer – Generative AI & LLM Applications

8–15 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.

Posted By