AI Enablement Lead
EverQuote Pro
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Job summary
EverQuote is seeking a hands-on Lead AI Enablement Engineer to drive the integration, usability and adoption of our enterprise AI services across Finance, Operations, HR and more. You will shape the roadmap, build connectors and ensure secure, trusted AI capabilities are embedded in daily workflows while mentoring others.
You will partner with IT, Security and Platform Engineering to deliver reliable, scalable services, focusing on practical value, governance and user empowerment.
Qualifications
- Extensive software development experience with a focus on AI technology.
- Hands-on engineering with integrations, internal tools or enterprise services.
- Experience applying LLMs to real workflows and understanding context, retrieval and evaluation.
Responsibilities
- Own the AI service roadmap and align with department leads.
- Integrate AI into enterprise workflows with secure, tested services.
- Lead end-to-end technical delivery and mentor others.
- Ensure end-user experience is reliable and easy to use.
Skills
Tools
Job description
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.
Location: Belfast
Department: Engineering Enablement
Reports to: VP Enablement
Employment Type: Full time
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.
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.
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.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
You put customers first: You observe how work actually happens, test changes with the people who will use them and stay involved until the solution is useful.
You are evidence-led: You evaluate new capabilities against real tasks, establish baselines with users and use evidence to improve or retire capabilities.
You stay curious: You experiment, follow developments in AI and separate changes worth adopting from those that add little value.
You own the outcome: You take responsibility for how a capability performs after launch, not only for getting it live.
You bring others with you: You mentor, set standards and communicate clearly with technical and non-technical colleagues, with departmental champions and with vendors.
In your first 90 days, you will have:
- Assessed our current AI services, feature availability, integration gaps and user experience.
- Agreed a prioritised roadmap.
- Delivered a small set of valuable integrations and reusable workflows across several departments.
- Established practical enablement sessions and a feedback loop.
- Sustained adoption across business functions.
- Reliable integrations.
- Better user satisfaction.
- Measurable improvements in task time or output quality.
You will establish baselines with users, track cost alongside benefit, and use evidence to improve or retire capabilities. Feature launches and licence activation alone are not measures of success.
At EverQuote, how we achieve results matters as much as the results themselves.
- Be Tenacious: We aim high, move quickly and persist through difficult technical and organisational problems.
- Help Customers Win: We prioritise work that creates meaningful and sustainable value for our customers.
- Follow the Data: We use evidence to guide decisions and measure outcomes.
- Strengthen Our Team: We communicate directly, share knowledge and leave teams and systems stronger than we found them.
EverQuote is committed to equal opportunity employment and to creating an inclusive environment for all employees. We welcome applicants from all backgrounds and consider qualified candidates without regard to characteristics protected by applicable law.
We are committed to ensuring an inclusive and accessible recruitment process for all candidates. If you require any reasonable adjustments or accommodations at any stage of the interview process, please let us know and we will work with you to arrange them.
or more information, visit https://investors.everquote.com and follow on LinkedIn.
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