Technical Product Owner, AI & Machine Learning Solutions

. Lead cross-functional teams of machine learning scientists, machine learning engineers, data scientists, and software engineers to deliver production-grade AI solutions .

Posted 11/6/2026full-timeRemote • CanadaMid-LevelSenior💰 CA$115,000 - CA$145,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading cross-functional teams to deliver AI and machine learning solutions, with a strong focus on architecture, implementation, and compliance. Possesses advanced technical proficiency in Python and SQL, alongside a deep understanding of MLOps and model risk management.

Highest-signal resume keywords

AI/ML Solution ArchitectureCross-Functional Team LeadershipPython ProgrammingMLOps PracticesModel Risk Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData ScienceGenerative AILarge Language ModelsModel Evaluation TechniquesData EngineeringSoftware Development PracticesSQLPythonDeployment Automation

Soft Skills

MentorshipStakeholder CollaborationTechnical Communication

Tools & Technologies

Version ControlCI/CDAutomated TestingObservability

Certifications & Qualifications

Advanced Degree in Computer ScienceAdvanced Degree in Artificial IntelligenceAdvanced Degree in Machine LearningAdvanced Degree in Data ScienceAdvanced Degree in EngineeringAdvanced Degree in Mathematics

Industry Keywords

AI GovernanceRegulated IndustriesOperational ResilienceAuditabilityModel Performance Trade-offs

Tech Stack

Tools & technologies

CloudPythonSQL

About the role

Key responsibilities & impact

Lead cross-functional teams of machine learning scientists, machine learning engineers, data scientists, and software engineers to deliver production-grade AI solutions
Work with stakeholders to identify, refine, and occasionally reject machine learning product opportunities
Collaborate with risk, technology, model risk management, and other support functions
Translate business objectives into technically feasible AI/ML solutions and articulate architecture, implementation, scalability, risk, and model-performance trade-offs
Develop the vision, strategy, and roadmap for AI technical products and capabilities
Maintain detailed understanding of solutions, architectures, and technical implementations to challenge design decisions and prioritize development
Facilitate colleagues’ professional and technical development through mentorship and feedback
Anticipate resource needs throughout the model lifecycle and scale pods as needed
Establish AI/ML engineering standards, best practices, and quality controls
Provide informed challenge on model design, MLOps, data architecture, and production-readiness decisions
Lead cloud-native AI/ML solution architecture and operational strategy definition
Partner with engineering and architecture teams on scalable deployment, monitoring, and lifecycle management of production AI systems
Ensure AI solutions meet standards for security, governance, model risk management, responsible AI, compliance, operational resilience, and auditability
Define, measure, and communicate product business value by connecting model performance to business outcomes and enterprise AI value targets

Requirements

What you’ll need

5+ years of experience delivering complex AI, machine learning, data science, advanced analytics, or software products in large-scale enterprise environments
3+ years of experience leading cross-functional technical delivery teams consisting of machine learning engineers, data scientists, software engineers, and MLOps practitioners
Deep understanding of machine learning, generative AI, large language models, retrieval systems, model evaluation techniques, data engineering, MLOps, and software development practices
Ability to evaluate technical designs and challenge architecture decisions while partnering effectively with engineering and research teams
Strong technical proficiency in Python and SQL
Knowledge of version control, CI/CD, automated testing, observability, and deployment automation
Familiarity with model risk management and AI governance in regulated industries
Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience delivering complex AI/ML solutions

Benefits

Comp & perks

Base salary and variable compensation
Health and well-being benefits
Savings and retirement programs
Paid time off
Banking benefits and discounts
Career development
Reward and recognition programs
Regular development conversations
Training programs
Online learning platform
Mentoring programs
Training and onboarding sessions
Interview accommodations, including accessible meeting rooms and captioning for virtual interviews