• Hands-on experience with LLMs such as OpenAI GPT, Claude, Gemini or Mistral
• Experience designing and building agentic AI solutions, including tool use, workflow orchestration, routing, memory, and multi-agent patterns
• Expertise in prompt engineering, structured outputs, and reliability patterns to optimise model performance
• Ability to implement evaluation frameworks for quality, grounding, task success, safety, latency, and cost
• Experience deploying AI solutions into production with focus on cost optimisation, scalability, observability, security, and governance
• Experienced in using Microsoft GenAI ecosystem, including M365, Copilot Studio, Azure AI Foundry, and Microsoft Agent Framework
• Proficient in Python and production-quality code practices
• Strong understanding of software engineering principles, architecture patterns, and delivery best practices
• Experience with API development using frameworks such as FastAPI, Flask, or Django
• Knowledge of Docker, containerised deployments, CI/CD, and cloud-native delivery patternsWhat will make you stand out
• Experience building, deploying, and evaluating multi-agent AI solutions, agent orchestration patterns, RAG architectures, and enterprise integrations
• Knowledge of agent communication protocols and interoperability patterns, such as MCP and A2A
• Ability to balance innovation with practical delivery considerations, including risk, cost, security, and production readiness
• Confidence contributing to pre-sales while also being able to lead implementation during delivery
• Curiosity and motivation to stay current with the latest advancements in GenAI, agentic AI, and AI engineering frameworksQualifications: