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Role & responsibilities 1. GenAI Technical Lead Role Purpose Lead the design, development, and delivery of enterprise-grade Generative AI and Agentic AI solutions. Drive technical execution, mentor engineering teams, and ensure scalable, secure, and production-ready AI deployments. Key Responsibilities Lead end-to-end delivery of GenAI and Agentic AI solutions. Design and implement RAG, AI Agents, MCP, multi-agent orchestration, and LLM-based applications. Guide teams in prompt engineering, evaluation frameworks, guardrails, observability, and performance optimization. Translate business requirements into scalable AI solutions. Establish coding standards, architecture patterns, and engineering best practices. Conduct technical reviews and mentor GenAI engineers. Collaborate with architects, product owners, and business stakeholders. Drive production readiness, monitoring, security, and Responsible AI compliance. Required Skills Strong hands-on experience with Python, FastAPI, LangChain, LangGraph, Semantic Kernel, AutoGen, MCP. Experience with Azure AI Foundry, OpenAI, AWS Bedrock or similar platforms. Strong understanding of LLMs RAG Vector Databases AI Agents Prompt Engineering LLM Evaluation AI Observability Experience with CI/CD, MLOps, containerization, and cloud-native deployments. Strong stakeholder communication and leadership abilities. Preferred candidate profile Experience 612 years overall 35 years in AI/GenAI Required Skills Strong hands-on experience with Python, FastAPI, LangChain, LangGraph, Semantic Kernel, AutoGen, MCP. Experience with Azure AI Foundry, OpenAI, AWS Bedrock or similar platforms. Strong understanding of LLMs RAG Vector Databases AI Agents Prompt Engineering LLM Evaluation AI Observability Experience with CI/CD, MLOps, containerization, and cloud-native deployments. Strong stakeholder communication and leadership abilities. Role & responsibilities 1. GenAI Technical Lead Role Purpose Lead the design, development, and delivery of enterprise-grade Generative AI and Agentic AI solutions. Drive technical execution, mentor engineering teams, and ensure scalable, secure, and production-ready AI deployments. Key Responsibilities Lead end-to-end delivery of GenAI and Agentic AI solutions. Design and implement RAG, AI Agents, MCP, multi-agent orchestration, and LLM-based applications. Guide teams in prompt engineering, evaluation frameworks, guardrails, observability, and performance optimization. Translate business requirements into scalable AI solutions. Establish coding standards, architecture patterns, and engineering best practices. Conduct technical reviews and mentor GenAI engineers. Collaborate with architects, product owners, and business stakeholders. Drive production readiness, monitoring, security, and Responsible AI compliance. Required Skills Strong hands-on experience with Python, FastAPI, LangChain, LangGraph, Semantic Kernel, AutoGen, MCP. Experience with Azure AI Foundry, OpenAI, AWS Bedrock or similar platforms. Strong understanding of LLMs RAG Vector Databases AI Agents Prompt Engineering LLM Evaluation AI Observability Experience with CI/CD, MLOps, containerization, and cloud-native deployments. Strong stakeholder communication and leadership abilities. Preferred candidate profile Experience 612 years overall 35 years in AI/GenAI Required Skills Strong hands-on experience with Python, FastAPI, LangChain, LangGraph, Semantic Kernel, AutoGen, MCP. Experience with Azure AI Foundry, OpenAI, AWS Bedrock or similar platforms. Strong understanding of LLMs RAG Vector Databases AI Agents Prompt Engineering LLM Evaluation AI Observability Experience with CI/CD, MLOps, containerization, and cloud-native deployments. Strong stakeholder communication and leadership abilities.