Oliver Wyman - Senior Lead AI Engineer

Marsh McLennan•Boston, NY

1d•Hybrid

About The Position

At Oliver Wyman Quotient, we partner with clients to deliver breakthrough outcomes for their toughest AI challenges. We blend the power of AI technology with deep industry expertise to tackle disruption and create impact. By building strong capabilities and culture, we accelerate and embed AI transformation. Our people co-create and grow customer-focused solutions that win. We modernize technology and harness value from data and analytics. We build resilience so our clients are ready for tomorrow’s risks and optimize operations for the future. Above all, we work collaboratively with our clients’ leaders, employees, stakeholders, and customers to jointly define, design, and achieve lasting results. As a Senior Lead AI Engineer, you are primarily responsible for managing technical projects and helping teams design and build AI/ML systems and pipelines that consistently drive solutions to be extensible and production quality. You are an expert in selected domains and can contrast methods and select appropriate approaches based on the data or modelling problem at hand. As an AI Engineer, you will work alongside Oliver Wyman partners in Quotient and other practice groups, engage directly with clients to understand their business challenges, and craft appropriate solutions to be delivered through collaboration with other OW Quotient specialists and consultants.

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, which deliver tangible, ongoing value including: Direct experience having built and deployed robust, complex production systems that implement modern artificial intelligence at scale
Comfort in environments where large projects are time-boxed, and therefore 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 modelling 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 is valued but not required
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.) is highly valued
Interest/background in Financial Services, Healthcare and Life Sciences, Consumer, Retail, Energy, or Transportation industries

Responsibilities

Exploring data and crafting AI solutions to answer core business problems
Working with Partners and Principals to shape proposals that leverage our AI and engineering capabilities
Building and deploying LLM-based agent systems in production
Keeping up with your domain’s state of the art & developing familiarity with emerging modelling and data engineering methodologies
Advocating application of best practices in modelling, code hygiene and data engineering
Leading the development of proprietary AI/ML solutions, algorithms, or analytical tools and infrastructure on projects and asset development