AI Engineer – Quotient AI Specialist

Marsh McLennan

. Build AI agent and LLM-based solutions supporting business development and project execution .

Posted 10/26/2026full-timeMadrid • SpainMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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

Demonstrates expertise in building AI agent and LLM-based solutions, with strong proficiency in Python and modern AI/ML tooling. Capable of translating business needs into technical execution while collaborating effectively across software engineering, data science, and AI specializations.

Highest-signal resume keywords

Fluency In PythonExperience Shipping LLM-Based SolutionsStrong Grounding In Mathematics Of MLExperience Working With Agent FrameworksAbility To Make Practical Design Decisions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Agent DevelopmentLLM Application PatternsMachine Learning SolutionsStatistical AnalysisLinear AlgebraSoftware Engineering PracticesTesting And Code HygieneDeployment TechniquesModelling TechniquesData Science

Soft Skills

CollaborationRapid Decision-MakingComfort In Fast-Moving EnvironmentsClient EngagementCommunication

Industry Keywords

BankingFinancial ServicesOpen-Source ContributionsProductizationScalable Solutions

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Build AI agent and LLM-based solutions supporting business development and project execution
Develop traditional ML and analytical models where appropriate
Prototype delivery formats including agents, avatars, and visual formats
Build reusable and scalable solutions, coordinating with Quotient Inside for productization
Translate business needs into technical execution with the Technical Business Partner
Collaborate with the Platform / Infrastructure Engineer on reliable, maintainable deployment
Pair with the Front-end / AI Content Creator on client-facing outputs and presentation formats
Join client discussions when technical credibility must be demonstrated or explained
Move rapidly from experimentation to delivery while maintaining quality
Contribute across software engineering, data science, and core AI / agent specializations
Participate in screening, technical exercise, team-fit, and craft interviews if shortlisted

Requirements

What you’ll need

Fluency in Python and modern AI / ML tooling across the build lifecycle
Hands-on experience shipping LLM-based or ML solutions, ideally under time pressure
Data-science-leaning track: strong grounding in the mathematics of ML, including statistics and linear algebra, plus a range of modelling techniques
Software-engineering-leaning track: strong engineering practice, including testing, code hygiene, and deployment
Ability to make practical design decisions quickly and keep moving
Experience building solutions that are useful in real-world settings, not only in experiments
Full professional English
Spanish welcome
Interest or background in Banking & Financial Services
Open-source contributions or compelling side projects
Experience working with agent frameworks, LLM application patterns, or data / AI workloads in production
Experience building solutions designed for reuse, scale, or productization
Comfort working in a fast-moving environment with multiple concurrent priorities
Submit a CV and cover letter

Benefits

Comp & perks

Hybrid work with flexibility to work remotely and collaborate in the office
Working in the local office or onsite with clients at least three days per week
Professional development benefits
Meaningful work, talented people, and a culture built on trust, support, and growth
Opportunities for learning, visibility, and growth inside a newly created team
Exposure to shaping a new capability from the ground up
Inclusive, collaborative, and respectful work environment
CV and cover letter application process with transparent interview process