Lead AI Enablement Engineer

Lead AI Enablement Engineer Location: Belfast Department: Engineering Enablement Reports to: VP Enablement Employment Type: Full time About EverQuote EverQuote’s vision is to make it more efficient to shop for and buy insurance with confidence. We use data, technology and marketplace expertise to connect insurance shoppers with providers and help our customers grow their businesses. As AI becomes part of everyday work across the organisation, we want every colleague, technical or not, to get real value from it. Our enablement teams make that possible by turning new AI capabilities into reliable, trusted services.

About the team

The Lead AI Enablement Engineer sits within Engineering Enablement and owns the integration, usability and adoption of EverQuote’s enterprise AI services. You will also partner with IT, Security and Platform Engineering on service support and controls, and with data owners on access, privacy and auditability.

About the role

Help everyone across our organisation get more value from AI. We are looking for a hands-on Lead AI Enablement Engineer to make our AI products and services connected, reliable and useful in everyday work across Finance, Operations, HR, Sales, Engineering and beyond. We already use Claude, and there is significant opportunity to improve the experience through relevant features, connectors, skills and access to trusted knowledge. You will turn that opportunity into working services and practical ways of working that colleagues adopt and value, while evolving our capabilities as AI technology advances. You will lead the technical integration and continuous improvement of these services, combining engineering depth with curiosity about LLMs and a strong customer focus.

The role

combines hands-on delivery with technical leadership and mentoring. You will shape how an entire organisation works with AI. You will have the opportunity to explore new capabilities, turn promising ideas into dependable services and see the impact directly in colleagues’ daily work. Your technical decisions and customer insight will help define a new AI capability and how it evolves. What you’ll do: Own the AI service roadmap: Understand how colleagues work and identify friction. Prioritise improvements with department leads. Treat technical and non-technical employees as customers, and test changes with them. Integrate AI into everyday work Configure and administer enterprise AI products. Work closely with other enablement teams to make sure the tooling is fully integrated. Work closely with InfoSec to make sure risks and standards have been met. Build connectors, MCP integrations, APIs and lightweight services that connect approved tools, business systems and knowledge sources. Keep permissions and data access aligned with users’ existing entitlements. Turn new capabilities into useful services Track model releases, product features and emerging tools. Evaluate quality, usability, cost and risk against real tasks. Recommend, pilot and roll out improvements with clear guidance and a way to reverse unsuccessful changes. Create reusable capabilities Make skills, shared instructions, workflow templates and agents discoverable, with clear owners, testing and version control. Help teams adapt proven approaches to their own work. Show colleagues what is possible Run practical demonstrations, workshops, office hours and hands-on sessions using real business workflows. Build a network of departmental champions. Make guidance accessible to different confidence and skill levels. Own the end-user experience Monitor integration health, answer quality and user feedback. Investigate failures and recurring friction. Coordinate fixes, vendor escalations, change communications and support arrangements with IT, Security and Platform Engineering. Provide technical leadership Establish integration and evaluation standards. Mentor others and shape vendor decisions. Continuously keep up to date with the latest LLM releases and harnesses to better inform the direction of the organisation. Partner with Security and data owners on access, privacy, auditability and safe tool use. What we’re looking for: 10 years experience in software development with 3+ years in AI Strong hands-on engineering experience building and supporting integrations, internal tools or enterprise services: you write maintainable code, work with APIs and authentication, and diagnose problems across connected systems. Practical experience applying LLMs and AI tools to real workflows used by other people, with an understanding of context, retrieval, tool use and evaluation, the limits of model outputs, and when human review is needed. A sustained interest in AI tooling, demonstrated through experimentation and working solutions. You follow developments actively and can explain which changes are worth adopting, which need more testing and which add little value. A customer-focused approach: you listen, observe how work happens and stay involved until the solution is useful. You can support a non-technical colleague and discuss integration trade-offs with an engineer. Evidence of technical leadership, prioritisation and cross-functional delivery: you can turn broad needs into a practical roadmap, set standards and bring others with you through coaching and clear communication. Experience delivering reliable services with appropriate access controls, testing, monitoring and documentation, and taking ownership of how a capability performs after launch. You do not need experience with every product or tool listed, but experience in several would be valuable: Claude Enterprise or similar enterprise AI products. MCP, reusable skills and agent workflows. Enterprise search and knowledge integration. Python or TypeScript. AWS. SaaS administration, SSO and OAuth. AI adoption programmes and communities of practice. How you work You put customers first: You observe how work actually happens, test changes with the people who will use them a