Technical Lead

Experience: 3 to 5 years

Location: Hyderabad, India

Skills: Python, FastAPI, Flask, LangChain, LangGraph, LlamaIndex, OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, Vertex AI, RAG, OpenSearch, Pinecone, Qdrant, Weaviate, pgvector, FAISS, Azure AI Studio, Copilot Studio, Azure Cognitive Search, SageMaker, AWS nova, AWS Transform, RAGAS, DeepEval, Mem0, LangMem

Job Summary
AI Lead Engineer – Generative AI & LLM Applications Agentic AI, langchain and langgraph, hands-on experience, especially in building and deploying multi-agentic systems and application memory management (Agentic AI systems). hands-on experience with Genai Applications, specifically involving Agents (memory management, agent middleware, orchestration), and MCPs and tool use. Experience Required 3– 5 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 recent 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,

Key Responsibilities
1. To be responsible for providing technical guidance / solutions; define, advocate, and implement best practices and coding standards for the team.
2. To develop and guide the team members in enhancing their technical capabilities and increasing productivity.
3. To ensure process compliance in the assigned module| and participate in technical discussions/review as a technical consultant for feasibility study (technical alternatives, best packages, supporting architecture best practices, technical risks, breakdown into components, estimations).
4. To prepare and submit status reports for minimizing exposure and risks on the project or closure of escalations.

Skill Requirements
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Other Requirements
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📌 Technical Lead (Secunderabad)
🏢 HCL Technologies
📍 Secunderabad