Oliver Wyman - Senior Lead AI Engineer

Marsh McLennan•Washington, DC

2d•$195,000 - $250,000•Hybrid

About The Position

Oliver Wyman Quotient partners with clients to address challenging AI problems, combining AI technology with industry expertise to create impact. The practice builds organizational AI capabilities, modernizes technology, harnesses data and analytics, strengthens resilience, and optimizes operations in collaboration with clients and their stakeholders. The Senior Lead AI Engineer manages technical projects and helps teams design and build extensible, production-quality AI/ML systems and pipelines. The role applies expertise in selected domains to assess data and modeling problems and choose appropriate approaches. The engineer works with Oliver Wyman partners and other practice groups, engages directly with clients to understand business challenges, and develops solutions collaboratively with Quotient specialists and consultants. The position is being hired across Oliver Wyman offices, including New York, Boston, Chicago, and Washington, DC.

Requirements

Technical background in computer science, data science, machine learning, artificial intelligence, statistics, or another quantitative and computational science.
Track record designing and deploying large-scale technical solutions that deliver tangible, ongoing value.
Direct experience building and deploying robust, complex production systems that implement modern artificial intelligence at scale.
Comfort working on time-boxed large projects where consequential design decisions may need to be made and acted on rapidly.
Fluency in modern programming languages used for AI and agents across the end-to-end AI development lifecycle.
Knowledge of one or more agentic platforms, such as Azure Foundry, AWS Bedrock, or Gemini Enterprise Agent Platform.
Deep familiarity with the architecture, performance characteristics, and limitations of compound AI systems, including retrieval-augmented generation, tool use, agentic memory, and multi-agent orchestration.
Experience designing and operating evaluation frameworks and observability tools to measure AI system quality, detect failure modes, and maintain production performance.
Proficiency in LLM system design, including prompt engineering, context and token management, structured output, fine-tuning tradeoffs, and cost and latency optimization.
Practical awareness of AI risk, bias, and governance, including identifying and mitigating failure modes in high-stakes or regulated environments.
Theoretical grounding in core AI/ML mathematics, including statistics, linear algebra, and vector calculus.
Applied understanding of a class of modeling or analytical techniques.

Nice To Haves

Compelling side projects or contributions to the open-source community.
Experience presenting at high-impact AI/ML engineering conferences and strong connections to the AI/ML engineering community, such as through meetups, open-source contributions, or continuing relationships with academics.
Interest or background in Financial Services, Healthcare and Life Sciences, Consumer, Retail, Energy, or Transportation industries.

Responsibilities

Explore data and develop AI solutions to address core business problems.
Work with Partners and Principals to shape proposals that use AI and engineering capabilities.
Build and deploy LLM-based agent systems in production.
Keep current with developments in the relevant domain and develop familiarity with emerging modeling and data engineering methodologies.
Advocate for best practices in modeling, code hygiene, and data engineering.
Lead development of proprietary AI/ML solutions, algorithms, analytical tools, and infrastructure on projects and for asset development.
Manage technical projects and help teams design and build extensible, production-quality AI/ML systems and pipelines.
Engage directly with clients to understand business challenges and collaborate on suitable solutions.