hireVouch

hireVouch is a premier recruitment and digital transformation solutions company dedicated to helping organizations find and retain top-tier talent. With a focus on hiring the right

Technical Product Manager - AI

Location

Canada

Posted

9 days ago

Salary

0

Seniority

Lead

No structured requirement data.

Job Description

Technical Product Manager - AI

hireVouch

Role Description As a Senior Technical Product Manager, you will help translate scientific research, machine learning development, and cloud-based engineering into a clear, production-focused roadmap that leadership and external stakeholders can rely on for planning and execution. The work connects applied research, software delivery, and commercial readiness into a single, visible operating model. As the company scales its platforms and enterprise-facing systems, you will establish the execution frameworks, dependency management practices, and communication layers that allow complex technical progress to be understood, sequenced, and delivered with confidence. Key Responsibilities - Execution framework ownership: Establish and maintain progress tracking systems and dashboards that provide leadership with clear visibility into development velocity and milestone delivery on a recurring basis. - Requirements translation: Partner with business and commercial stakeholders to convert customer needs and market signals into well-defined technical requirements for engineering and data teams. - Executive partnership: Serve as a technical advisor to senior leadership, communicating tradeoffs, risks, and timelines in a way that supports go-to-market planning and decision-making. - Architecture alignment: Collaborate with engineering teams to ensure cloud-native systems and data platforms can support large-scale data processing and model deployment. - Infrastructure strategy support: Contribute to the evolution of machine learning service infrastructure to meet performance, reliability, and scalability needs for enterprise-facing use cases. - Dependency management: Coordinate the lifecycle of interconnected data pipelines, models, and services, ensuring upstream and downstream changes are communicated and integrated across teams. - Sprint and ritual leadership: Lead day-to-day execution practices, ensuring work is well-scoped, documented, and tracked through consistent engineering workflows. - Cross-team orchestration: Manage critical paths across data engineering, machine learning, and cloud infrastructure to prevent delivery blockers and maintain development momentum. - Resource and priority sequencing: Support leadership in making informed decisions about technical investments, tradeoffs, and the ordering of major initiatives. Tech Stack - Data and ML systems: Large-scale data ingestion pipelines, machine learning pipelines, model deployment infrastructure. - Program and delivery tools: Engineering ticketing systems, dashboarding and reporting tools, sprint and workflow management platforms. Qualifications - 5+ years owning product delivery in deeply technical domains (data platforms, ML products, infrastructure-heavy systems, or developer/platform products). - Experience leading or managing programs involving large-scale data platforms, machine learning systems, and cloud infrastructure. - Able to translate complex technical and scientific work into clear, business-focused updates for senior stakeholders and leadership. - Ability to operate in early-stage ambiguity while building the foundations enterprise customers expect around delivery, documentation, reporting, executive communications, and operational maturity. - Ability to reason about system design, data flows, and ML lifecycle enough to ask sharp questions, spot gaps, and drive clarity. - Experience translating ML work into product outcomes, including understanding datasets, model iteration cycles, evaluation metrics, monitoring, model drift, and the realities of deploying and operating ML in production. - Comfortable working with cloud-native architectures and the tradeoffs involved in scaling ingestion, compute, storage, latency, reliability, and cost. - Ability to create crisp requirements that reduce ambiguity (well-scoped epics, user stories, acceptance criteria) and definitions of done that teams can actually execute against. It's a Bonus If - Experience working with data collected from sensor-based, geospatial, or field-deployed systems. - Worked with (or adjacent to) atmospheric modeling, forecasting systems, or high-stakes environmental prediction. - Experience supporting government customers, utilities, or regulated enterprises, including longer buying cycles, compliance requirements, and high standards for documentation and auditability. - Owned the implementation of reporting frameworks for board-level and executive-level technical visibility.

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