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 business processes, data, and technology estates. This applied engineering role focuses on production AI systems rather than research.

Key responsibilities

• Lead AI solution delivery from problem framing through production release and continuous improvement.
• Design pragmatic architectures that connect AI capabilities with existing applications, APIs, data platforms, identity, security, and operational controls.
• Develop and review critical components, establish coding and testing standards, and guide engineering trade‑offs.
• Build production AI systems using agentic applications, retrieval‑augmented generation, function calling, structured outputs, and multi‑agent workflows.
• Engineer inference architectures, model routing, caching, semantic retrieval, vector databases, evaluation pipelines, observability, and cost optimization.
• Define evaluation criteria, test sets, observability, and feedback loops for accuracy, reliability, safety, latency, cost and business impact.
• Collaborate with security, privacy, risk and legal partners to implement responsible AI guardrails.
• Mentor engineers, contribute reusable patterns, and promote AI engineering best practices.

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.
• Hands‑on experience implementing AI‑enabled solutions such as agentic applications, RAG or intelligent workflow automation.
• Strong communication skills and ability to translate ambiguous business needs into feasible technical approaches.

Required skills

• Python programming.
• Experience with at least one enterprise stack: Java.NET or TypeScript/Node.js.
• Cloud platform expertise (AWS, Azure or Google Cloud).
• Knowledge of CI/CD, automated testing, version control and observability.
• Familiarity with Kubernetes and containerized workloads.
• SQL and data integration concepts.