Python Ai Developer Jobs in Denver, CO (NOW HIRING)

$200,000 - $300,000 Total Compensation 

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. 

Our reach is global, with headquarters in Denver, Colorado, and offices across the U.S., Canada, and India. 

About the Role

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.

What You'll Do

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
• 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

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

Required 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

Preferred Qualifications

• 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

Why This Role

You'll have real ownership — of a team, of a technical domain, and of shipped product used by insurance professionals across North America. You'll have leadership who is fully committed to delivering real AI value to customers and invest in your growth. And you'll be operating in an industry that is genuinely early in its AI transformation, with the data, infrastructure, and customer relationships already in place to make the work matter.