Oliver Wyman - Senior Lead Engineer

Marsh McLennan•Washington, DC

5d•$195,000 - $250,000•Hybrid

About The Position

At Oliver Wyman Quotient, we partner with clients to deliver breakthrough outcomes for their toughest AI challenges by blending AI technology with deep industry expertise. We accelerate and embed AI transformation by building strong capabilities and culture, co-creating customer-focused solutions, modernizing technology, and harnessing value from data and analytics. We build resilience for future risks and optimize operations. We work collaboratively with clients' leaders, employees, stakeholders, and customers to jointly define, design, and achieve lasting results.

Requirements

Technical background in computer science, data science, machine learning, artificial intelligence, statistics, or other quantitative and computational science.
Compelling track record of 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 in environments where large projects are time-boxed, and consequential design decisions may need to be made and acted upon rapidly.
Demonstrated fluency in modern programming languages for artificial intelligence/agents, covering the end-to-end AI development lifecycle.
Knowledge of one or more agentic platforms, including but not limited to: Azure Foundry, AWS Bedrock, 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 tooling to measure AI system quality, detect failure modes, and maintain performance in production.
Proficiency in LLM system design, including prompt engineering, context and token management, structured output, fine-tuning tradeoffs, and cost/latency optimization.
Practical awareness of AI risk, bias, and governance, including how to identify and mitigate failure modes in high-stakes or regulated environments.
Solid theoretical grounding in the mathematical core of the major ideas in AI/ML.
Applied understanding of a class of modeling or analytical techniques.
Fluency in the mathematical principles and generalizations of data science – e.g., Statistics, Linear Algebra, and Vector Calculus.

Nice To Haves

A history of compelling side projects or contributions to the Open-Source community.
Experience presenting at high-impact AI/ML engineering conferences and solid connections to the AI/ML engineering community (e.g., via meetups, open-source contributions, continuing relationships with academics, etc.).
Interest/background in Financial Services, Healthcare and Life Sciences, Consumer, Retail, Energy, or Transportation industries.

Responsibilities

Managing technical projects and helping teams design and build AI/ML systems and pipelines that are extensible and production quality.
Exploring data and crafting AI solutions to answer core business problems.
Working with Partners and Principals to shape proposals that leverage AI and engineering capabilities.
Building and deploying LLM-based agent systems in production.
Keeping up with the state of the art in the domain and developing familiarity with emerging modeling and data engineering methodologies.
Advocating for the application of best practices in modeling, code hygiene, and data engineering.
Leading the development of proprietary AI/ML solutions, algorithms, or analytical tools and infrastructure on projects and asset development.