- Translate product and engineering challenges into AI-driven solutions that enhance speed, quality, and outcomes
- Build and deploy AI Agents with advanced reasoning, integrating memory, MCP, custom MCP servers, and A2A
- Apply prompt engineering, context engineering, AI steering, RAG, Chain of Thought, ReAct, and other modern AI frameworks to real-world use cases
- Partner with product and engineering teams to embed AI, LLMOps, and observability into requirements, coding, testing, monitoring, and operations
- Prototype, test, optimise, fine-tune, and scale AI solutions, balancing experimentation with production readiness and inference deployment
- Design, run, and automate evals to test LLM outputs for quality, reliability, and safety
- Implement security guardrails and robust data integration across agentic workflows to mitigate vulnerabilities
- Support pre-sales and client discussions by demonstrating applied AI use cases and outcomes
- Stay ahead of research and practice in GenAI and bring them into daily engineering practice
- Communicate findings and trade-offs clearly to both technical teams and executives
PythonPrompt EngineeringGenerative AI