Oliver Wyman - Senior Lead AI Engineer

Company:

Oliver Wyman

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Description:

LOCATIONS

We are hiring across our OW offices including but not limited to: New York, Boston, Chicago, DC, Toronto and Montreal.

PRACTICE OVERVIEW

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.

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THE ROLE AND RESPONSIBILITIES

No two OW Quotient projects are the same. You’ll be working with varied and diverse teams to deliver unique and unprecedented products across industries. 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.

Your responsibilities will include, for example:

• 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

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YOUR EXPERIENCE & QUALIFICATIONS

You are a well-rounded technologist who brings a wealth of real-world experience and:

• 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
  • A history of compelling side projects or contributions to the Open-Source community is valued but not required
• 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 an…