Note: The job is a remote job and is open to candidates in USA. Verifast verifies identity, income, and employment for property management, mortgage, and KYC clients across the US and Canada. The AI Product Engineer will own product problems end to end, build AI-powered features and workflows, collaborate with clients and internal teams, and measure the effectiveness of shipped solutions.
Responsibilities
- Own problems end to end, from discovery through launch and iteration. Often you will be the one defining the problem, not just solving it
- Run discovery directly with clients, Client Success, Support, and Operations. Sit in on calls, watch how people actually work, and find where time and accuracy are being lost
- Decide what's worth building. Size the opportunity, weigh effort against impact, and be willing to argue that something on the roadmap shouldn't be built
- Write clear specs and requirements that engineering, support, and leadership can all work from
- Define what success looks like before you build, in numbers. Track it after launch and be honest about whether it worked
- Contribute to roadmap and prioritization discussions with a point of view backed by evidence
- Navigate the tradeoffs that come with a regulated product, where compliance requirements,client-specific rules, and PMS integration constraints all limit what a clean solution can look like
- Design, build, test, and ship AI-powered features and workflows. This includes prompt design, tool and function definitions, evaluation, and the surrounding application logic
- Build evaluation sets and test cases so we know whether a solution actually works before it touches a client. Real accuracy numbers, not vibes
- Prototype fast and iterate in front of real users. Get a rough version working in days, not a perfect version in months
- Partner with engineering on anything that needs to move into the production platform, and document your work so it can be maintained by others
- Monitor what you ship. Track accuracy, failure modes, cost, and adoption after launch, and improve it
Skills
- • 3 or more years in a product role: product manager, technical product owner, product operations, solutions or forward-deployed engineer, or founder. You've owned features or products end to end, not just executed a backlog someone else wrote
- • A track record of shipped work you can point to and explain: what problem it solved, what you decided not to build, how you knew it worked
- • Strong product judgment. You can look at a workflow, identify where the actual pain is, and propose something better rather than automating the existing process as-is
- • Experience turning direct customer conversations into product decisions, and comfort saying no to a request when the underlying need is better served another way
- • Demonstrated ability to build working software with AI assistance. You've shipped something real
- • Structured, logical thinking. You can decompose a fuzzy problem into steps, identify edge cases before they bite, and reason about where a system will fail
- • Hands-on experience with LLMs beyond casual use: prompt engineering, tool use, chaining, RAG, or agent workflows
- • Enough technical fluency to work independently: reading and modifying code, understanding APIs and JSON, basic SQL, and using version control
- • Excellent written communication. Much of this job is writing clearly, whether that's a prompt, a spec, or an explanation for a client
- • Comfort with ambiguity and a bias toward shipping
- To be direct about where the bar sits: you don't need to be able to architect a platform from scratch, but you do need to be able to build and debug independently
- Concretely, you should be able to stand up a working application that calls an LLM API, connects to a database, handles errors, and runs somewhere other than your laptop, without needing an engineer to unblock you at every step
- • B2B SaaS product experience, particularly with enterprise clients who have strong opinions about workflow
- • Experience in PropTech, fintech, identity verification, or another regulated data environment
- • Familiarity with Snowflake, Python, TypeScript, or React
- • Experience working alongside design, or the ability to produce a usable interface without one
- • Experience building evaluations or QA processes for AI systems
Benefits
- This is a full-time position with hybrid or remote work options.
- Eligible employees receive a health expense reimbursement benefit in accordance with the Company's applicable reimbursement policy.
Company Overview