Head of AI Engineering 1026

You’ll partner closely with our VP of Product, who owns the product roadmap, while you own the architecture and engineering that bring it to life. Expect to spend most of your time designing, building, and reviewing code, and to know when a model is the right answer and when a SQL query is.

AI is the center of this job, not a side project. You’ll shape how a lean, fully remote company builds, ships, and works with AI with access to the latest and greatest tools.

What You’ll Own

• AI-First Architecture: Own the company’s system and AI architecture. Design platforms where models, agents, and traditional services work together, and make the calls on speed, cost, and scale.
• AI Product Delivery: Lead the design and build of AI-powered features from prototype to production: LLM integrations, document intelligence, retrieval (RAG), and agentic workflows that automate real work across the loan lifecycle.
• AI-Native Engineering: Make AI coding assistants and agents the default way the team designs, builds, tests, and documents software, and measure whether they’re actually making us faster and better.
• AI Experimentation: Keep the company on the front edge. Test new models, tools, and techniques quickly, share what you learn, and turn the winners into product.
• AI Champion: Be the company’s go-to voice on AI, internally and with customers. Help teams outside engineering put AI to work, and explain what it can and can’t do in plain English.
• Responsible AI: Own model evaluation, monitoring, data privacy, and human-in-the-loop controls. Partner with Compliance on the company’s ISO 42001 AI management system and the expectations of a highly regulated industry.
• Team Leadership: Lead a team of traditional and AI engineers as a player-coach, and help every engineer level up their AI skills. Set clear goals, give direct feedback, pair on hard problems, and build a team people want to stay on.
• Product Partnership: Work hand in hand with the VP of Product to turn the roadmap into technical plans, with honest input on feasibility, sequencing, and effort.
• Hands-On Building: Write production code, build prototypes, and lead by example in code reviews and design discussions.
• Delivery: Run the end-to-end development process, from requirements through deployment, with predictable timelines and honest status updates.
• Quality and Security: Set and enforce standards for code quality, testing, security, and scalability.
• Cross-Functional Partnership: Work with customer success, sales, and leadership to understand what customers need, and explain technical tradeoffs in plain English.
• Talent: Partner with People Operations to hire, develop, and retain strong engineers, including AI and ML specialists, as the team grows. Set performance goals and run regular reviews.
• Vendors and Partners: Evaluate AI models, platforms, and development partners, and manage cost, performance, and lock-in risk.
• Documentation: Keep project documentation and time tracking current in Teamwork.

About You

• AI in Production: A real track record shipping production features built on large language models (e.g., Anthropic Claude, OpenAI, AWS Bedrock), including prompt design, retrieval, evaluation, and cost control. Show us what you’ve built.
• AI Fluency: You use AI tools every day, have opinions about what works, and can explain the difference between a good demo and a reliable product.
• AI Enthusiasm: You’re genuinely excited about where AI is headed and keep up with new models, tools, and techniques, without chasing every shiny thing.
• Agentic Systems: Experience designing agents and multi-step workflows with tool use, MCP, and guardrails, using frameworks like LangChain and LangGraph, plus a clear view of where humans need to stay in the loop.
• Experience: 10+ years in software engineering, including significant time as a principal, staff, or lead architect, 2+ years building with LLMs or machine learning in production, and 3+ years leading engineering teams, ideally remote.
• Hands-On: You still write code, and you like it. You’re as comfortable in a pull request as in a design review.
• Responsible AI: Working knowledge of AI risk, model governance, and data privacy. Familiarity with ISO 42001, NIST AI RMF, or similar frameworks is a plus.
• Technical Depth: Deep experience in system design, modern web technologies, cloud platforms (AWS preferred), APIs, and scalable architecture. Proficiency in .NET, Node.js, Python, or similar.
• Leadership: A player-coach track record: mentoring engineers, delivering on commitments, and building a culture of ownership without a lot of process.
• Project Management: Experience turning product requirements into technical plans and estimates that hold up.
• Communication: Able to explain complex technical concepts to engineers, executives, and customers alike.
• Adaptability: Comfortable in a fast-moving, bootstrapped company where priorities shift and resourcefulness counts.
• Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field, or equivalent experience.
• Bonus Points: A GitHub repo, side project, talk, or blog post that shows how you think about AI.

Tech Stack

• LLM platforms and APIs (Anthropic Claude, OpenAI, AWS Bedrock)
• Agent frameworks and Model Context Protocol (MCP)
• Hands-on experience with LangGraph (or similar)
• Retrieval, embeddings, and vector search
• LLM evaluation and observability tooling
• AI coding assistants (e.g., Claude Code, GitHub Copilot)
• .NET / Node.js / Python
• HTML/CSS/JavaScript and React
• RESTful and GraphQL APIs, XML/JSON
• SQL / NoSQL
• AWS
• Teamwork project management, Agile delivery
• Bonus points for BI (DAX, Power BI)