Oliver Wyman - Senior Lead AI Engineer - Melbourne
Senior AI engineering lead at Oliver Wyman (a Marsh McLennan business) in Melbourne, designing and delivering production AI solutions for enterprise clients — agentic apps, RAG, MCP and AI platforms integrated with existing systems, APIs and data. Hands-on Python plus an enterprise stack, delivered in a hybrid 3-days-in-office model.
- Compensation
- Not disclosed
- Location
- Leigh Creek, Moorabool Area
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Full time
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Must have
- Typically 10-12 years professional experience in software engineering, solution architecture, platform engineering or related enterprise technology roles
- Track record designing and delivering production enterprise applications, integrations or digital platforms in complex, regulated or security-conscious environments
- Recent hands-on experience implementing AI-enabled solutions (agentic applications, RAG, intelligent workflow automation or ML services)
- Strong software engineering fundamentals: API design, distributed systems, automated testing, version control, CI/CD, observability, secure development
- Proficiency in Python plus practical experience with at least one enterprise stack (Java, .NET or TypeScript/Node.js)
- Experience delivering on at least one major cloud platform (AWS, Azure or Google Cloud)
- Working knowledge of enterprise data integration, SQL, search/retrieval and handling structured and unstructured data
- Practical understanding of model/application evaluation, prompt and context design, privacy, security, responsible AI controls and production monitoring
- Experience leading technical work across multidisciplinary teams, mentoring engineers and influencing architecture/engineering standards
- Clear communication and commercial judgment to translate ambiguous business needs into feasible technical approaches
- Appropriate approval to work in Australia
Nice to have
- Consulting, professional services or client-facing technology delivery experience
- Experience in a regulated industry or with enterprise risk, compliance and governance processes
- Familiarity with AI application frameworks (LangGraph, PydanticAI) or model platforms (Azure Foundry, AWS Bedrock)
- Experience operating containerized workloads or working with Kubernetes and infrastructure as code
- Degree in computer science, engineering or related discipline, or equivalent professional experience
Full description
Full time
Hybrid
4mo ago , from Marsh & McLennan Companies MMC
At Oliver Wyman, a Marsh (NYSE: MRSH) business, we bring deep industry insight, bold innovation, and a collaborative approach that cuts through complexity to help organizations navigate their most defining transformative moments.
As a business of Marsh, we work alongside the world’s leading experts across risk, reinsurance and capital, people and investments, and management consulting. Together with Marsh Risk, Guy Carpenter, and Mercer, we help organizations build resilience and competitive advantages from every angle. With annualrevenueof$27billionand more than 95,000 colleagues in 130 countries, Marsh helps buildthe confidenceto thrive through the power of perspective.
The opportunity
We are looking for a senior technology leader who has built and modernized enterprise applications and now applies that engineering foundation to AI-enabled products. You will lead the design and delivery of secure, reliable AI solutions that integrate with real business processes, data and technology estates.
This is an applied engineering role, not a research-science position. Success requires strong software architecture and delivery judgment, practical experience with modern AI capabilities, and the credibility to guide engineers and senior stakeholders from opportunity definition through production adoption.
What you will do
- Lead AI solution delivery.Own the technical journey from problem framing and feasibility assessment through architecture, implementation, production release and continuous improvement.
- Design for the enterprise.Create pragmatic architectures that connect AI capabilities with existing applications, APIs, data platforms, identity, security and operational controls.
- Build and guide hands‑on engineering.Develop or review critical components, establish coding and testing standards, and help teams make sound trade‑offs across quality, cost, speed and maintainability.
- Build production AI systems. Design and implement agentic applications using agentic harnesses, retrieval‑augmented generation (RAG), MCP, function/tool calling, structured outputs, and multi‑agent workflows where appropriate.
- Engineer AI platforms for production. Design inference architectures, model routing, caching, semantic retrieval, vector databases, evaluation pipelines, observability, and cost optimization to support enterprise‑scale AI applications.
- Make quality measurable.Define evaluation criteria, test sets, observability and feedback loops for accuracy, reliability, safety, latency, cost and business impact.