Full-Time

Senior Lead AI Engineer

Quotient

Posted on 8/17/2026

Marsh & McLennan

Marsh & McLennan

Risk, insurance and business consulting

Compensation Overview

$195k - $250k/yr

+ Performance-based incentives

Company Does Not Provide H1B Sponsorship

Montreal, QC, Canada + 3 more

More locations: Boston, MA, USA | Toronto, ON, Canada | New York, NY, USA

Hybrid

At least three days per week in the local office or at client sites.

Category
Software Engineering (1)
Required Skills
LLM
Data Science
Machine Learning
Data Engineering
RAG
Requirements
  • A technical background in computer science, data science, machine learning, artificial intelligence, statistics, or another quantitative and computational science discipline.
  • A 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 implementing modern artificial intelligence at scale.
  • Ability to make and act on consequential design decisions rapidly in time-boxed large-project environments.
  • Fluency in modern programming languages for artificial intelligence and agent systems 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 system architecture, performance characteristics, and limitations, 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 production performance.
  • Proficiency in large language model system design, including prompt engineering, context and token management, structured output, fine-tuning tradeoffs, and cost and 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 artificial intelligence and machine learning concepts.
  • Applied understanding of a class of modeling or analytical techniques.
  • Fluency in the mathematical principles and generalizations of data science, including statistics, linear algebra, and vector calculus.
Responsibilities
  • Explore data and craft artificial intelligence solutions to answer core business problems.
  • Work with Partners and Principals to shape proposals that leverage artificial intelligence and engineering capabilities.
  • Build and deploy large language model-based agent systems in production.
  • Keep current with the state of the art in the relevant domain and develop familiarity with emerging modeling and data engineering methodologies.
  • Advocate for best practices in modeling, code hygiene, and data engineering.
  • Lead the development of proprietary artificial intelligence and machine learning solutions, algorithms, analytical tools, and infrastructure for projects and asset development.
  • Manage technical projects and help teams design and build extensible, production-quality artificial intelligence and machine learning systems and pipelines.
  • Engage directly with clients to understand business challenges and craft appropriate solutions in collaboration with specialists and consultants.
Desired Qualifications
  • Side projects or contributions to the open-source community.
  • Experience presenting at high-impact artificial intelligence and machine learning engineering conferences and strong connections to the artificial intelligence and machine learning engineering community.
  • Interest or background in financial services, healthcare and life sciences, consumer, retail, energy, or transportation industries.

Marsh, formerly Marsh McLennan, is a global professional services firm focused on risk, insurance, reinsurance, talent and business strategy. Its operating businesses help organizations arrange coverage, model and transfer risk, design workforce programs and address complex management questions. Clients range from growing companies to governments and multinational enterprises. The group combines advisory expertise with insurance-market access and data, making it broader than an insurance broker alone while remaining primarily a business-to-business services organization.

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