Senior AI Engineer

A path for every ambition.

At HUB, we believe the best work happens when people are given real space to grow and then given the autonomy to exercise it. We power progress and protect ambition: for the clients who count on us, and equally for the people who make that possible every day.

We’re creative thinkers and determined doers, guided by values that show up in how we work together: with empathy, adaptability, accountability and a genuine commitment to one another’s success. We foster bold ideas, celebrate curiosity and share what we know. Because when people grow, the work they do grows with them.

Our purpose is deeply human: to help people navigate the world on their terms, at their speed, toward their future. We hold that same commitment to the people building careers here.

Whether you’re starting out or leveling up, you’ll find the resources to evolve, the support to take on more and a team ready to back you at every stage.

Your ambition has a home here.

About Us

At HUB International, we are a team of entrepreneurs. We believe in protecting and supporting the aspirations of individuals, families, and businesses. We help our clients evaluate their risks and develop solutions tailored to their needs. We believe in empowering our employees to learn, grow, and make a difference. Our structure enables our teams to maintain their own unique, regional culture while leveraging support and resources from our corporate centers of excellence.

HUB is a global insurance and employee benefits broker, providing a boundaryless array of business insurance, employee benefits, risk services, personal insurance, retirement, and private wealth management products and services. With over $5 billion in revenue and almost 20,000 employees in 600 offices throughout North America, HUB has grown substantially, in part due to our industry leading success in mergers and acquisitions.

Position Overview

HUB is seeking a Senior AI Engineer to serve as the senior technical individual contributor for the AI Engineering function within our centralized technology organization, part of HUB’s AI & Automation organization. This is the most senior hands-on engineering seat in the function. The role builds the hardest work, sets the technical standards the rest of the team builds to, and owns the enterprise standards and governance for HUB’s skills, plug-ins, and MCP connections wherever they are used across HUB.

This is an individual contributor role. It carries no direct reports. It does carry technical authority, and the person in it is expected to lead through standards, architecture, code review, and mentoring rather than through reporting lines. A significant portion of that influence extends to engineers outside the function, including builds executed by other engineering teams to the standard this role sets.

The role builds on the Claude AI platform: creating reusable skills and plug-ins, composing them into governed AI applications, standing up retrieval over governed enterprise data, and professionalizing experimental prototypes into governed, tested, enterprise-ready applications. HUB runs Claude as its primary model today and the stack is expected to evolve toward agentic systems. Where a build requires deep engineering, such as net-new backend, new infrastructure, or complex integration, Enterprise Engineering executes it to the standard this role sets and returns it to the catalog to be governed and published. This role does not build or host foundational models and does not perform core machine learning or model training.

How this differs from the AI Engineer role. AI Engineers build and ship against an established standard. This role establishes that standard, owns the technical decisions behind it, resolves the problems the team cannot, and is accountable for the reliability and governance of the function’s output as a whole.

Key Responsibilities

Build the Hardest Work

• •Build reusable skills, package them as plug-ins, and compose them into governed AI applications used across HUB business lines
• •Configure and tune foundational models for HUB use cases, including system and prompt design
• •Build retrieval-augmented generation, embeddings, vector store, and multi-step workflow implementations over governed enterprise data
• •Take business-built AI prototypes and productionize them into reliable, governed applications
• •Own the engineering decisions behind cost, latency, and reliability tradeoffs across the AI estate

Set the Technical Standard

• •Define and document the engineering standards the function builds to, covering architecture patterns, testing, logging, documentation, and release discipline
• •Review architecture and code across the team, and raise the quality of what ships through that review
• •Mentor AI Engineers and contract engineers, deliberately transferring knowledge so the function does not depend on any one person
• •Set and uphold the enterprise standards and governance for skills, plug-ins, and MCP wherever they are used across HUB, on any surface, including builds executed by other engineering teams
• •Partner with Enterprise Engineering on deep builds, defining the standard those builds must meet before they return to the catalog

Own the AI Asset Platform

• •Own the technical operation of the skill and plug-in marketplace and the MCP catalog: intake, publish, version, maintain, and deprecate
• •Apply access control, least-privilege, and audit to AI assets, partnering with Security, Privacy, and Cloud on identity and credentials
• •Ensure every AI asset published to the enterprise has a defined owner, documented behavior, and a support path

Quality, Evaluation, and Governance

• •Stand up and own evaluation harnesses, golden-set testing, guardrails, monitoring, and logging so AI output is consistent and reliable at enterprise scale
• •Establish acceptance criteria and regression gating so a change that fixes one case does not silently break others
• •Provide production support for the skills, plug-ins, and applications the function releases, including incident response for non-deterministic systems
• •Translate governance, security, privacy, and regulatory requirements into engineering controls and audit-ready evidence

Collaboration and Delivery

• •Partner with business stakeholders and the product function to turn prototyped ideas into production-ready, governed capabilities
• •Partner with Enterprise Engineering, Data and Analytics, Cloud, and Security to ship reliably and route deep builds appropriately
• •Represent AI Engineering in technical and governance forums
• •Work within Agile ceremonies and document skills, applications, and technical decisions for handoff, audit, and maintenance

Required Qualifications

• •7+ years of software or automation engineering, including 3+ years building and shipping applications backed by large language models to production
• •Direct, recent, hands-on experience building and publishing Claude skills or plug-ins. Broad experience with large language models or frameworks, without shipped skills and plug-ins, is not sufficient.
• •Demonstrated technical leadership as an individual contributor: setting standards other engineers followed, reviewing others’ architecture and code, and mentoring engineers without managing them
• •Strong Python, REST APIs, and enterprise integration, with sound fundamentals in testing, version control, and monitoring
• •Hands-on experience with foundational models: system and prompt design, skill and tool building, function and tool calling
• •Retrieval-augmented generation, embeddings, and vector stores, including chunking and reranking
• •Evaluation and monitoring for systems backed by large language models, with a working feel for cost, latency, and reliability tradeoffs
• •Experience operating inside a regulated enterprise, with security, governance, and access control as part of normal delivery
• •Cloud experience (Azure or GCP / Vertex AI)
• •Bachelor’s degree in Com