Principal Product Engineer: AI Systems

Location Calgary, AB (hybrid). Open to remote for exceptional candidates. Term Full-time

Own the product surface end to end. Turn complex, stateful system behavior into interfaces a cautious enterprise user can trust and act on.

Enterprise teams do not act on AI unless they trust it.

We are building products where AI outputs are verifiable, actions are controlled, and every step is traceable. The system needs to show not just what happened, but why, and whether it is safe to act.

This is not a layer on top of AI tools or a workflow for power users. The goal is to build enterprise-grade products teams rely on daily, with the consistency, visibility, and control expected in production systems.

The underlying systems are complex, stateful, and often messy. The product needs to make that understandable without hiding it.

MCP is an important part of the system underneath. You will work closely with MCP-connected capabilities, agent workflows, and backend system behavior, then turn that complexity into product experiences users can trust.

The goal is not "pretty UI." It is building something a cautious, experienced enterprise user can trust and use as part of their daily work.

You would own the product surface end to end, from structure to interaction.

Who you'd work with

A small senior team. You would work closely with the founder on product direction and with the engineering team on shipping. Direct access, no layers, decisions happen in the conversation you are part of.

What you'd do in your first 90 days

Day to day, you will work primarily in React and TypeScript, design interfaces for complex data and workflows, and decide what users see, in what order, and at what level of detail.

The challenge is not rendering data. It is deciding what matters, what to show, and what can be trusted. You will be working with real system constraints, not clean datasets.

• Weeks 1–2: Learn the product, backend APIs, MCP-connected capabilities, problem space, and the users we are building for.
• Month 1: Ship your first end-to-end feature. Likely something that turns raw system output into a structured, verifiable workflow a user can act on.
• Months 2–3: Own a significant part of the product surface. Set direction on UX patterns, design systems, and how the product scales from early use to production.

What you need to be good at

• Senior full-stack experience, with products used in real production environments
• Turning complex, system-level data into interfaces that feel simple and usable
• Strong product judgment. You can define scope and make decisions without a PM
• Experience building workflows, not just screens, including approvals, states, evidence, and traceability
• Using AI coding tools such as Claude Code, Cursor, and GitHub Copilot with judgment

Experience working with developer platforms, system integrations, or workflow-heavy enterprise products is a plus. SAP or enterprise ERP experience is great to have, not required.

What you'd pick up here

• How enterprise products differ from internal tools or power-user workflows
• Designing UX for AI-assisted systems where trust, auditability, and control matter
• How MCP-connected capabilities and agent workflows should appear in user-facing products
• Working in a team where product and engineering are tightly integrated

The deal

• Competitive market rate.
• Milestone bonuses.
• Real ownership of your domain.