About the company

Our hiring partner builds enterprise software for learning, talent, and workforce teams. Their platform helps large organizations plan work, manage capacity, coordinate execution, and measure impact across complex learning and people operations.

The company is entering a major product and technology transformation: rebuilding its core platform for an AI-native future, while expanding intelligent capabilities that connect internal teams, AI systems, and external experts in a more scalable operating model.

The opportunity

This is a hands-on Principal/Staff-level engineering role for someone who combines platform architecture, senior technical judgment, and modern AI-native development practices.

You will help define the technical foundation for a rebuilt enterprise platform, while also demonstrating what high-leverage, agent-augmented engineering looks like in day-to-day execution. This is not a purely advisory architecture role: the right person will write production code, build prototypes, review designs, solve difficult technical problems, and help raise the engineering bar across the team.

What you’ll do

• Own architecture for an AI-native enterprise platform, including agent-friendly APIs, structured event streams, clean data contracts, retrieval-ready data layers, and production-grade observability.
• Design systems that can support enterprise requirements such as security, privacy, role-based access, auditability, tenant isolation, SSO, data residency, and compliance expectations.
• Define scalable data models, APIs, and platform patterns that make intelligent product features easier to build, govern, and extend over time.
• Make thoughtful tradeoffs between speed, simplicity, quality, and scale, and document key decisions so the broader team can execute with clarity.
• Build and maintain production-grade features while setting strong standards for code quality, reliability, performance, and operational ownership.
• Use AI-assisted development workflows deeply and pragmatically across coding, testing, debugging, documentation, research, and code review.
• Evaluate AI-generated output critically, knowing when to accept, refactor, discard, or add stronger human review.
• Mentor senior engineers and technical leads in architecture, engineering judgment, design reviews, code reviews, testing practices, and ownership.
• Help establish practical standards and guardrails for AI use in software development, including where human review is required and how to catch common failure modes.
• Partner closely with product, data science, and engineering leadership to turn ambiguous product and business goals into clear technical direction.
• Support senior hiring and interview calibration by raising the quality bar for AI-native and platform-oriented engineering talent.

What we’re looking for

• Proven experience as a Staff Engineer, Principal Engineer, senior technical lead, or equivalent senior IC in a modern SaaS environment.
• Strong track record leading major product, platform, or architecture rebuilds, ideally for enterprise customers with security, compliance, and governance needs.
• Experience designing AI-native or AI-augmented products, including patterns such as agent-friendly interfaces, structured data flows, retrieval-ready data, observability, and responsible production operations.
• Daily hands-on use of AI coding tools and assistants such as Cursor, GitHub Copilot, Claude, GPT-based workflows, or similar tools as part of a real engineering workflow.
• Practical experience designing or orchestrating agentic systems, multi-agent workflows, AI-assisted development pipelines, or production AI-enabled software systems.
• Deep backend and systems fundamentals, including API design, data modeling, distributed systems, performance, reliability, observability, and security.
• Experience with cloud infrastructure, CI/CD, automated testing, and production operations.
• Strong written and verbal communication, especially around design docs, technical tradeoffs, async collaboration, and teaching through clear examples.
• A genuine interest in helping other engineers level up, not just increasing your own output.

What success looks like

• The rebuilt platform has a stronger enterprise-ready technical foundation and clearer patterns for AI-native product development.
• Engineering teams can ship intelligent features faster without creating avoidable security, governance, or technical debt.
• AI-assisted development becomes a practical, well-reviewed part of how the team works, because others can see effective examples in action.
• Senior engineers make better technical decisions, design reviews become sharper, and the overall engineering bar rises.

Logistics

• Full-time role.
• Remote, with preference for candidates based in Canada.
• Some travel may be required for key team or leadership sessions.
• Competitive compensation package.