DI – Expert Python, GenAI – LLM (CE815SF RM 3882)

Position: DI – Expert Python, GenAI – LLM (CE815SF RM 3882)

Primary skills: Expert-level Python, GenAI Frameworks, LLM Integration, RAG & Search, Vector Databases, Cloud & AI Services
Secondary skills: 8–15 years in AI/ML development, with 3+ years specialized in Generative AI and LLM applications.

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.
• 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:
  o Azure: Azure OpenAI, Azure AI Studio, Copilot Studio, Azure Cognitive Search.
  o AWS: Bedrock, SageMaker endpoints, AWS nova, AWS Transform etc.
  o GCP: Vertex AI (models, endpoints), Agent space, 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.

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