AI Engineering Leader

About the role

The insurance industry runs on Vertafore. We equip agencies, MGAs, and carriers with the core digital systems, specialized AI, and data-driven foundation to eliminate distribution drag across the insurance lifecycle, spanning sales, servicing, and back-office operations. Underpinned by unmatched speed and performance power, we are the trusted backbone that’s taking the insurance industry from friction to flow with Distribution Velocity - speed, performance, and trust - to drive growth at scale. With over 95% of the top agencies and insurers and 50% of industry compliance transactions running through Vertafore, we lead at the intersection of innovation and trust, giving insurance professionals the confidence to transform and win in the AI era.

Responsibilities

• Lead a High-Performing AI Engineering Team
• Manage, mentor, and grow a team of 4-8 engineers.
• Running 1:1s, setting technical direction, and creating clear paths for individual development.
• Foster a culture of rigor, fast iteration, and creative problem solving.
• Build a team where engineers are empowered to raise problems early and move from experiment to production without unnecessary friction.
• Partner with recruiting to hire strong AI engineering talent and retain it through meaningful work and clear growth.
• Own Technical Delivery for Your Domain
• Drive the end-to-end delivery of AI features and systems within your team's scope, from design to deployment to post-production observability.
• Own Technical Delivery for Your Domain
• Drive the end-to-end delivery of AI features and systems within your team's scope, from design to deployment to post-production observability.
• Work closely with a product manager to translate customer problems and business priorities into a well-sequenced engineering roadmap.
• Stay Hands-On at the Right Level
• Contribute meaningfully to system design, code reviews, and debugging when it matters with a focus on greenfield work, critical path systems, or when the team is blocked.
• Set and uphold engineering standards for your team: testing strategy, evaluation frameworks, model observability, and responsible deployment practices.
• Know when to write the code yourself and when your highest-leverage move is unblocking someone else.
• Build and Operate Production AI Systems
• Oversee the design and operation of LLM-powered features, ETL pipelines, agentic workflows, and/or document extraction systems.
• Maintain a high bar for production quality: latency, cost, reliability, and behavioral consistency across a diverse multi-tenant customer base.
• Build feedback loops and monitoring that detect model drift, degraded outputs, and edge case failures before customers do.
• Collaborate Across the Organization
• Work with peer engineering teams, platform engineering, and data engineering to share infrastructure, avoid duplication, and raise the AI platform's overall capability.
• Communicate clearly to surface risks early, quantify tradeoffs, and clear a path to rapid development and iteration for you team.
• Occasionally engage directly with customers, customer success, or implementation teams to ground your team's work in real-world usage patterns.

Qualifications

• 5+ years of experience in machine learning, AI engineering, or applied data science.
• 1-3+ years of direct people management or formal tech lead experience with responsibility for team delivery.
• Hands-on production experience with one or more of: LLM-powered applications, agentic workflows, document extraction pipelines, or classical ML systems.
• Proficiency in Python and familiarity with the modern AI engineering stack — LangChain/LangGraph (or equivalent), vector databases, prompt engineering, and model evaluation tooling.
• Experience deploying and operating AI systems in a cloud environment (AWS preferred).
• Strong written and verbal communication skills — able to write a crisp design doc, run a productive design review, and give clear status to non-technical stakeholders.
• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related quantitative field.