R

Research Engineering Design Canada

Automates enterprise software deployment and configuration

Principal Product Manager

Full-Time
CA$150k - CA$190k/yr
Expert
Bachelor's
Toronto, ON, Canada+2 moreMore locations: Waterloo, ON, Canada | Vancouver, BC, Canada
RemoteRemote within Canada; occasional in-person collaboration and regular business travel up to 25% are expected.
ProductTechnical Product Management

About the job

Requirements
  • A bachelor's degree in Computer Science, Engineering, Business, or equivalent education or work experience.
  • At least 8 years of product management experience, including end-to-end ownership of B2B SaaS products or platforms and leadership across multiple products, initiatives, or teams.
  • Experience shipping AI or large language model-powered features to production, with an understanding of quality evaluation, failure modes, and cost management.
  • Experience with developer platforms, enterprise software, or workflow and low-code products, including products that other teams build on.
  • Experience in government, the public sector, or regulated industries where auditability, privacy, and procurement shape product decisions.
  • Technical fluency to understand architecture documents, application programming interface specifications, and pull requests, and contribute effectively to design reviews.
  • Practical proficiency with AI tools in day-to-day product work, including prototyping, analysis, research synthesis, and specification writing; ability to evaluate outputs critically, protect sensitive information, and apply human judgment.
  • Demonstrated entrepreneurial initiative, ownership, and communication skills; ability to manage concurrent priorities, learn beyond existing expertise, and work directly with customers.
  • Authorized to work in Canada without sponsorship and willing and able to undertake occasional, reasonable business travel.
Responsibilities
  • Lead continuous discovery across products with customers, developers, and business users, including public-sector and regulated-industry teams; turn interviews, workshops, usability sessions, and usage data into clear problem statements.
  • Identify workflows where AI can deliver measurable value, such as case triage, routing, document search, and work order drafting; validate demand and feasibility before committing delivery capacity.
  • Track customer needs, competitors, agent frameworks, model providers, and relevant policy and regulatory developments; assess their implications across the product portfolio.
  • Anticipate market shifts and identify opportunities for new products or capabilities; test assumptions early and use evidence to recommend where to invest.
  • Develop hands-on knowledge across the products led to run demonstrations, configure pilots, and build working prototypes with limited engineering support; understand where existing capabilities meet customer needs and where investment is required.
  • Define requirements for AI-enabled products and workflows, including tasks, agent roles, permissions, human review, audit trails, and feedback loops.
  • Guide reusable capabilities and developer tooling where they improve delivery across products; balance shared standards with the needs of individual customers and markets.
  • Partner with engineering on architecture decisions affecting security, privacy, tenancy, reliability, or auditability; identify scale and edge-case risks early and agree on mitigations.
  • Own product strategies and coordinated 6- to 12-month roadmaps across multiple products and projects; prioritize investment and sequence work against customer value, business objectives, dependencies, delivery capacity, and risk.
  • Lead build, buy, hybrid, and sunset recommendations, supported by customer evidence, cost, expected return, and the alternatives considered.
  • Decide which capabilities should be shared across products and which should remain product-specific, considering reuse, delivery speed, quality, and maintenance cost.
  • Set longer-term direction and contingency plans for model and vendor dependencies, data residency, and procurement constraints; help shape positioning, go-to-market plans, and pricing for new products.
  • Define and report product success measures, including adoption, customer outcomes, time to value, delivery speed, quality, AI performance, and operating cost.
  • Design experiments and comparisons with clear success criteria; use results to guide investment and improve products after launch.
  • Write product requirements with measurable outcomes; coordinate execution across engineering, design, and other stakeholders with clear ownership, milestones, dependencies, and release readiness; manage scope and lead user acceptance testing and launch preparation.
  • Track risks, blockers, and changes across concurrent initiatives; resolve issues promptly and escalate material trade-offs before they affect customer commitments.
  • Partner with engineering and business leaders on capacity planning and return-on-investment assessments across teams; make priorities and resource constraints explicit, and adjust plans as conditions change.
  • Document decisions, trade-offs, and results in shared team materials, and revisit them against outcomes.
  • Present product direction and evidence to executives, engineers, portfolio companies, and customers, adjusting the depth and framing for each audience.
  • Align priorities across engineering, design, and business stakeholders; facilitate decisions, resolve competing needs, and secure agreement on scope and expectations.
  • Lead customer discovery sessions, demonstrations, workshops, and pilot reviews; translate feedback into actionable product decisions and communicate capabilities and delivery expectations clearly.
  • Build collaboration across teams and external partners, and represent company products in business reviews and the wider OpenText Ventures ecosystem.
  • Mentor product managers, co-ops, and designers, including coaching on discovery, product requirements, and evidence-based decisions.
  • Support recruiting and onboarding using the company's competency rubrics.
  • Create reusable playbooks for product intake, discovery, requirements, launch, and value assessment that improve work across teams.
  • Take initiative to identify gaps, learn unfamiliar domains, and lead work beyond established responsibilities when needed; exercise commercial judgment, collaborate with specialists, and model rigor, curiosity, and continuous improvement.
Desired Qualifications
  • Experience with agentic AI, including tool use, Model Context Protocol, agent skills, multi-agent orchestration, human-on-the-loop review, and evaluation methods.
  • Experience with workflow and case management, including workflow engines, state machines, approvals, service-level deadlines, and task assignment.
  • Experience with enterprise SaaS foundations, including multi-tenancy, identity and role-based access control, audit logging, and data residency.
  • Hands-on prototyping with AI coding tools such as Claude Code or Cursor, or with AI design tools.
  • Knowledge of the Canadian public-sector context, such as municipal services or access-to-information processes including ATIP and FIPPA/MFIPPA.
  • Experience with OpenText data platforms and the wider ecosystem, or with VC- or PE-backed portfolio companies.

About the company

R

Research Engineering Design Canada

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Research Engineering Design Canada (redcan.ai) builds agentic AI-driven tools and enterprise software deployment solutions that automate the deployment and configuration of complex business processes for B2B software companies. Its products automate workflows to speed time-to-value, reduce costs, and ease user adoption, while offering flexible engineering teams experienced with legacy systems and modern AI to support modernization and platform migration. The company focuses on intelligent autonomous systems that anticipate user needs and adapt in real time to support workflow journeys, using a tech stack including TypeScript, React, Node.js, and Docker for containerized deployments. Its goal is to help enterprise customers deploy software more efficiently, improve user satisfaction and retention, and accelerate modernization initiatives.

Company Size

11-50

Company Stage

N/A

Total Funding

N/A

Headquarters

N/A

Founded

N/A

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What believers are saying

  • Redcan.ai is still hiring in 2026, signaling active customer demand and execution.
  • LinkedIn shows employees spanning 2024-2026, suggesting retention and ongoing buildout.
  • The August 2026 website refresh confirms a live, current go-to-market motion.

What critics are saying

  • Public evidence shows only 11-50 employees and no disclosed funding runway.
  • If AI platform vendors bundle deployment automation, Redcan's niche margins compress quickly.
  • A services-heavy identity can erase product differentiation and cap enterprise valuation.

What makes Research Engineering Design Canada unique

  • Redcan.ai targets enterprise deployment automation, not generic horizontal AI tooling.
  • Its product-plus-services model combines agentic software with Canadian engineers for migrations.
  • Career posts stress legacy systems expertise plus TypeScript, React, Node.js, and Docker.

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