Senior Lead AI Engineer

Marsh McLennan

. Manage technical projects . Help teams design and build extensible, production-quality AI/ML systems and pipelines . Contrast methods and select appropriate approaches based on data or modelling problems . Work

Posted 11/4/2026full-timeUnited StatesSenior💰 $195,000 - $250,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying large-scale AI/ML systems, with a strong focus on LLM system design, prompt engineering, and data engineering best practices. Proven ability to engage with clients, understand business challenges, and craft tailored AI solutions that deliver measurable value.

Highest-signal resume keywords

AI/ML System DesignLarge-Scale Production SystemsLLM System DesignAgentic Platforms (Azure Foundry, AWS Bedrock)AI Risk and Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningArtificial IntelligenceStatisticsLinear AlgebraVector CalculusData EngineeringPrompt EngineeringModeling TechniquesEvaluation FrameworksObservability Tooling

Soft Skills

Client EngagementCollaborationRapid Decision MakingProblem SolvingAdvocacy for Best Practices

Tools & Technologies

Azure FoundryAWS BedrockGemini Enterprise Agent PlatformAI System Quality ToolsAnalytical Tools

Industry Keywords

Financial ServicesHealthcare and Life SciencesConsumerRetailEnergyTransportation

Tech Stack

Tools & technologies

AWSAzure

About the role

Key responsibilities & impact

Manage technical projects
Help teams design and build extensible, production-quality AI/ML systems and pipelines
Contrast methods and select appropriate approaches based on data or modelling problems
Work alongside Oliver Wyman partners in Quotient and other practice groups
Engage directly with clients to understand business challenges
Craft appropriate solutions in collaboration with OW Quotient specialists and consultants
Explore data and craft AI solutions addressing core business problems
Work with Partners and Principals to shape proposals leveraging AI and engineering capabilities
Build and deploy LLM-based agent systems in production
Track the state of the art and develop familiarity with emerging modelling and data engineering methodologies
Advocate best practices in modelling, code hygiene, and data engineering
Lead development of proprietary AI/ML solutions, algorithms, analytical tools, and infrastructure on projects and asset development

Requirements

What you’ll need

Technical background in computer science, data science, machine learning, artificial intelligence, statistics, or another quantitative and computational science
Compelling track record designing and deploying large-scale technical solutions delivering tangible, ongoing value
Direct experience building and deploying robust, complex production systems implementing modern artificial intelligence at scale
Comfort working on large, time-boxed projects requiring rapid consequential design decisions
Fluency in modern programming languages for artificial intelligence and agents across the end-to-end AI development lifecycle
Knowledge of one or more agentic platforms, including Azure Foundry, AWS Bedrock, or Gemini Enterprise Agent Platform
Deep familiarity with compound AI systems, including retrieval-augmented generation, tool use, agentic memory, and multi-agent orchestration
Experience designing and operating evaluation frameworks and observability tooling for AI system quality, failure-mode detection, and production performance
Proficiency in LLM system design, prompt engineering, context and token management, structured output, fine-tuning tradeoffs, and cost/latency optimization
Practical awareness of AI risk, bias, and governance, including identifying and mitigating failure modes in high-stakes or regulated environments
Solid theoretical grounding in the mathematical foundations of major AI/ML concepts
Applied understanding of a class of modelling or analytical techniques
Fluency in statistics, linear algebra, and vector calculus
Interest or background in Financial Services, Healthcare and Life Sciences, Consumer, Retail, Energy, or Transportation industries

Benefits

Comp & perks

Performance-based incentives
Health and welfare benefits
Tuition assistance
401K savings
Other retirement programs
Employee assistance programs
Flexible work environment
Remote-work flexibility
Collaboration, connections and professional development benefits of working together in the office