Forward Deployment Engineer (FDE)

AI Lead Engineer Generative AI & LLM Applications

Experience Required

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

• Masters 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.