AI Engineering Leader

Vertafore•Remote, CO

1d•Onsite

About The Position

Vertafore is seeking a hands-on AI Engineering Leader to own delivery for a core AI product domain. The role manages a small team while remaining actively involved in technical work, including architecture, code reviews, technical decisions, and coding. The team’s production systems include AI features such as agentic workflows, intelligent document processing, and predictive analytics, serving agencies and carriers across North America. The leader is accountable for building and enabling the team and for delivering reliable, scalable AI products.

Requirements

At least 5 years of experience in machine learning, AI engineering, or applied data science.
At least 1 year of direct people management or formal tech lead experience with responsibility for team delivery; the posting specifies 1–3+ years.
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, including LangChain/LangGraph or equivalent, vector databases, prompt engineering, and model evaluation tooling.
Experience deploying and operating AI systems in a cloud environment; AWS is preferred.
Strong written and verbal communication skills, including writing design documents, leading design reviews, and providing clear status to non-technical stakeholders.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related quantitative field.
Legally authorized to work in the United States.
Able to meet the physical requirements listed: occasional lifting and/or moving up to 10 pounds; frequent repetitive hand and arm movements to operate a computer; close vision; and frequent sitting and/or standing.
Available for travel up to 10% of the time and work from an office location.

Nice To Haves

Experience in B2B SaaS, insurance, financial services, or another regulated vertical.
Familiarity with MLOps tooling.
Experience with intelligent document processing, such as parsing PDFs, structured tables, or semi-structured data at scale.
Experience with LLM observability and AI gateway tooling such as LangSmith, Helicone, Portkey, or 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; conduct 1:1s, set technical direction, and support individual development.
Foster a culture of rigor, fast iteration, creative problem solving, and early escalation of problems.
Partner with recruiting to hire and retain AI engineering talent.
Drive end-to-end delivery of AI features and systems, from design through deployment and post-production observability.
Own technical direction for the domain, including scope estimates, architectural decisions, tradeoff documentation, and post-mortems.
Partner with a product manager to translate customer problems and business priorities into an engineering roadmap.
Participate in system design, code reviews, and debugging, especially for greenfield work, critical-path systems, and team blockers.
Set engineering standards for testing, evaluation frameworks, model observability, and responsible deployment.
Oversee the design and operation of LLM-powered features, ETL pipelines, agentic workflows, and/or document extraction systems.
Maintain production quality in latency, cost, reliability, and behavioral consistency across a multi-tenant customer base.
Build feedback loops and monitoring to identify model drift, degraded outputs, and edge-case failures.
Collaborate with peer engineering, platform engineering, and data engineering teams to share infrastructure and improve AI platform capabilities.
Communicate risks and tradeoffs clearly to support rapid development and iteration.
Occasionally engage with customers, customer success, or implementation teams to understand real-world usage patterns.