About the role
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 optimisation.
- 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.
Requirements:
- 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.