Senior Lead AI Engineer

About the role

Oliver Wyman is seeking a senior technology leader to design and deliver secure, reliable AI solutions that integrate with enterprise applications, data platforms and business processes. This applied engineering position focuses on production AI systems rather than research.

Key responsibilities

• Lead AI solution delivery from problem framing through architecture, implementation, production release and continuous improvement.
• Design pragmatic enterprise architectures that connect AI capabilities with existing APIs, data platforms, identity, security and operational controls.
• Develop critical components, set coding and testing standards, and guide teams on trade‑offs between quality, cost, speed and maintainability.
• Build production AI systems using agentic harnesses, retrieval‑augmented generation, function calling, structured outputs and multi‑agent workflows.
• Engineer inference architectures, model routing, caching, vector databases, evaluation pipelines, observability and cost optimisation for enterprise‑scale AI.
• Define evaluation criteria, test sets and feedback loops to measure accuracy, reliability, safety, latency, cost and business impact.
• Collaborate with security, privacy, risk and legal partners to embed responsible AI guardrails and auditability.

Required profile

• 10‑12 years of experience in software engineering, solution architecture or platform engineering.
• Proven track record delivering production enterprise applications in complex, regulated or security‑focused environments.
• Recent hands‑on experience building AI‑enabled solutions such as agentic applications, RAG or intelligent workflow automation.
• Strong fundamentals in API design, distributed systems, automated testing, CI/CD, observability and secure development.
• Clear communication skills and ability to translate ambiguous business needs into feasible technical approaches.

Required skills

• Python
• Java.NET or TypeScript/Node.js (at least one enterprise stack)
• Experience with a major cloud platform (AWS, Azure or Google Cloud)
• Kubernetes and containerised workloads
• SQL and enterprise data integration concepts
• CI/CD pipelines and version control