Required Skills and Experience
Proven AI transformation experience, including recent, personal delivery of production LLM applications and agents, preferably in regulated or security-sensitive environments.
Deep AI architecture expertise spanning model selection, orchestration, data and context layers, identity, integration, and observability.
Hands-on experience with multi-agent systems and Agent-to-Agent (A2A) interoperability, Model Context Protocol (MCP) servers and clients, and AI gateways for routing, access controls, guardrails, and cost governance.
Strong practical knowledge of RAG, GraphRAG, context engineering, agent memory, and agent frameworks or SDKs; experience applying AI coding assistants and autonomous agents across the SDLC.
Expertise in LLMOps, evaluation, regression testing, tracing, production monitoring, model adaptation, and inference optimization.
Experience implementing defenses against prompt injection and tool misuse, human approval controls, and responsible AI governance.
Hands-on experience with at least one major cloud AI platform—Azure AI Foundry, AWS Bedrock, or Google Vertex AI—and leading model-provider APIs.
Strong executive communication, team leadership, and commercial judgment, with the ability to translate complex technology into compelling business value.
Success in This Role
Secure, scalable AI adoption that moves beyond experimentation, delivers measurable engineering and business improvements, and maintains continuity from strategy and deal commitments through production execution.
AI Tools -
- Azure AI Foundry
- Agent365
- Copilot
- MS Purview
- Defender