AI Engineering Leader

Vertafore•Denver, CO

About The Position

Vertafore's AI organization is moving fast — from agentic workflows to intelligent document processing and predictive analytics. These systems are in production, serving thousands of agencies and carriers across North America, and the bar for reliability, scalability, and craft is high. We're looking for an AI Engineering Leader who leads from the front. You'll own delivery for one of our core AI product domains, managing a small team of engineers while staying close enough to the work that you're the person your team turns to when the architecture gets hard. You’ll be a hands on leader with expertise in designing, evaluating, and shipping AI based features. This is not a role for someone who has stepped fully away from the code. You are an active participant in the team, reviewing PR’s, making technical decisions, and writing code. Your value multiplier is the team you build and enable, but your credibility comes from the depth you bring.

Requirements

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

Nice To Haves

Experience in B2B SaaS, insurance, financial services, or another regulated vertical
Familiarity with MLOps tooling
Exposure to intelligent document processing, for example parsing PDFs, structured tables, or semi-structured data at scale
Experience with LLM observability and AI gateway tooling (e.g., LangSmith, Helicone, Portkey, LiteLLM)
Background building systems with human-in-the-loop workflows and agentic task orchestration

Responsibilities

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
Drive the end-to-end delivery of AI features and systems within your team's scope, from design to deployment to post-production observability
Be the accountable technical voice for your domain: scope estimates, architectural decisions, tradeoff documentation, and post-mortems all run through you
Work closely with a product manager to translate customer problems and business priorities into a well-sequenced engineering roadmap
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
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
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