Senior AI Engineer
$130,000 - $170,000 per year
Apply (opens in a new tab)Senior AI Engineer
Posted yesterday
SENIORITY
Lead
SALARY
$130,000 - $170,000 per year
About the role
- Build the Hardest WorkBuild 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
- 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 Computer Science, Information Systems, Engineering, or a related technical field, or equivalent practical experience
- Agent development and orchestration (LangGraph, CrewAI, Claude Agent SDK), the growth path for this role
- Model Context Protocol design, MCP server governance, and agent-to-agent patterns
- Identity and access concepts: RBAC, non-human or agent identity, and least privilege
- Working fluency in an AI governance framework such as the NIST AI Risk Management Framework, ISO/IEC 42001, or the NAIC model bulletin on insurer use of AI systems
- Experience as the senior technical voice in a new or maturing capability, including standing up standards where none existed
- UiPath or Microsoft Power Platform exposure, given the automation estate this function supports
- Insurance or financial services industry experience, and familiarity with audit and control expectations in a pre-IPO or public company environment
- Familiarity with Git, CI/CD pipelines, and modern software engineering practices, and tools such as Jira or Azure DevOpsKey Competencies
- Genuine software engineering depth rather than prompt-only development
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
