Senior Lead AI Engineer

Marsh McLennan

. Explore data and craft AI solutions to answer core business problems .

Posted 11/3/2026full-timeUnited StatesSenior💰 $195,000 - $250,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying LLM-based AI systems, with a strong foundation in data science, machine learning, and artificial intelligence. Proficient in managing technical projects and advocating best practices in AI/ML development and data engineering.

Highest-signal resume keywords

LLM System DesignAI/ML Solution DevelopmentAgentic Platforms (Azure Foundry, AWS Bedrock)Statistical Analysis and Linear AlgebraAI Risk and Governance

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningArtificial IntelligenceData EngineeringStatisticsPrompt EngineeringModeling TechniquesEvaluation FrameworksObservability ToolingComplex Production SystemsMathematical Concepts in AI/ML

Soft Skills

Project ManagementCollaborationClient EngagementRapid Decision-MakingAdvocacy for Best Practices

Tools & Technologies

Azure FoundryAWS BedrockGemini Enterprise Agent PlatformAI/ML Engineering ToolsData Pipelines

Industry Keywords

Financial ServicesHealthcare and Life SciencesConsumerRetailEnergyTransportation

Tech Stack

Tools & technologies

AWSAzure

About the role

Key responsibilities & impact

Explore data and craft AI solutions to answer 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
Keep current with state-of-the-art developments and 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
Manage technical projects and help teams design and build extensible, production-quality AI/ML systems and pipelines
Work alongside Oliver Wyman partners and other practice groups
Engage directly with clients to understand business challenges
Craft appropriate solutions in collaboration with Quotient specialists and consultants

Requirements

What you’ll need

Technical background in computer science, data science, machine learning, artificial intelligence, statistics, or another quantitative and computational science
Track record of 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
Ability to make consequential design decisions rapidly in time-boxed large projects
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, retrieval-augmented generation, tool use, agentic memory, and multi-agent orchestration
Experience designing and operating evaluation frameworks and observability tooling for AI system quality 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 core 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
Experience presenting at high-impact AI/ML engineering conferences and connections to the AI/ML engineering community are highly valued

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