AI Product Engineer

Urban Ridge Supplies; Toronto; Hybrid; CAD 105,000 - 120,000; Full time

Job summary

VeriFast, headquartered in Toronto, Ontario, seeks an AI Product Engineer to build AI-powered workflows and client-facing features. You will own problems end to end—from discovery with clients to shipping and measuring impact—and work with Claude-like tools to transform client problems into working products.

This role blends product leadership with hands-on building, not research. The ideal candidate ships real solutions, demonstrates strong product judgment, and can turn vague business problems

Qualifications

• 3+ years in a product role with end-to-end ownership of features or products.
• Ability to translate customer conversations into product decisions.

Responsibilities

• Own problems end to end from discovery through launch and iteration.
• Run discovery directly with clients, Client Success, Support, and Operations.
• Decide what’s worth building and size opportunities.
• Write clear specs and requirements for engineering, support, and leadership.
• Define success metrics before building and track them after launch.
• Contribute to roadmap discussions with evidence-based perspective.

Skills

Product judgment

Discovery with clients

AI-enabled product design

Communication

Ambiguity tolerance

Independent coding & debugging

SQL basics

Prompt engineering

Decision making

Tools

Snowflake

Python

TypeScript

React

Job description

About us:

Verifast verifies identity, income, and employment for property management, mortgage, and KYC

clients across the US and Canada. A large amount of what we do involves judgment calls on messy

documents, edge-case applicant situations, and client-specific rules. Much of that work can now be

built rather than staffed.

We're hiring an AI Product Engineer to build those solutions. You will work primarily with Claude and

similar tools to turn client problems into working product: agents, automations, internal tools, and

customer-facing features.

This is a product role first and a building role second. You will own problems end to end: talking to

clients and internal teams to understand where the pain actually is, deciding what's worth building

and what isn't, shipping it, and measuring whether it worked. The building is what makes the product

work possible, not the other way around.

This is not a research role and it is not a traditional software engineering role. We care much more

about whether you can take a vague, messy business problem and ship something that actually solves

it than about whether you can pass a systems design interview. The best person for this job might be a

technical product manager who learned to build, a support or operations lead who automated their

own function, a founder whose startup didn't work out, or a self-taught engineer with unusually good

product instincts.

What you'll build:

These are real problems on our roadmap, not hypotheticals:

• Document intelligence. Pay stubs, bank statements, offer letters, tax transcripts, and benefits letters arrive in every possible format. Build extraction and validation that handles the long tail without a human touching it.
• Support automation. We run an AI support assistant and a Zendesk operation with tight SLAs.
• Build agents that resolve tickets end to end, enrich them with context from our data, and escalated correctly.
• Client-facing workflows. Leasing agents and property managers spend hours on repetitive review work. Build tools that compress that into minutes.
• Internal leverage. Reporting, reconciliation, QA, and data cleanup work that currently consumes analyst time. Automate it.
• Solutioning for clients. When a large client has a workflow problem that our standard product doesn't cover, scope it, prototype a solution, and get it in front of them quickly.

Responsibilities:

Product

• 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.

Building

• 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.

What we're looking for:

Required

• 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.
• We want to see it.
• 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.

Prefered:

• 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.

Not Required:

• A computer science degree.
• Years of professional software engineering experience.
• Deep infrastructure, DevOps, or distributed systems knowledge. We have engineers for that.

The Technical Floor

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.

If your instinct when something breaks is to read the error, form a hypothesis, and test it, you'll do

well here. If your instinct is to hand it to someone else, this is not the right role.

How we'll evaluate:

Instead of a whiteboard interview, we'll give you a real problem from our backlog with sample data

and ask you to build something over a few days. We'll then walk through it together: what you built,

what you tried that didn't work, what you'd do next, and where you think it would break at scale.

• We're evaluating how you think about the problem at least as much as what you produce.

Compensation, Benefits & Work Arrangement

• Location: VeriFast is headquartered in Toronto, Ontario.
• Work Arrangement: This is a full-time position with hybrid or remote work options.
• Compensation: $105,000–$120,000 CAD annually, based on experience.
• Health & Wellness: Eligible employees receive a health expense reimbursement benefit in accordance with the Company's applicable reimbursement policy.