Staff AI Product Manager

Accountabilities Define AI product strategy: Develop and own the vision, roadmap, and delivery strategy for enterprise generative AI and agentic AI solutions, translating business objectives into clear priorities and measurable outcomes.

Lead product discovery and validation: Research enterprise user needs, identify adoption barriers, formulate testable hypotheses, and build prototypes to validate assumptions before committing to full-scale development.

Drive AI architecture decisions: Collaborate with engineering teams to evaluate multi-model architectures and balance quality, cost, latency, scalability, and reliability across AI-powered workflows.

Design and optimize AI agents: Define agent behavior, system prompts, tool schemas, context management, memory strategies, human-in-the-loop controls, safety guardrails, and failure recovery mechanisms.

Manage product delivery: Own backlogs, prioritize competing workstreams, refine requirements, coordinate sprint execution, and keep cross-functional teams focused on delivering production-ready solutions efficiently.

Establish evaluation and measurement frameworks: Develop and maintain evaluation processes that assess task completion, reasoning quality, tool selection, safety, and recovery from failures. Define success metrics covering quality, cost per task, user trust, and escalation rates.

Ensure production readiness: Partner with engineering teams to establish observability, drift detection, prompt versioning, and other operational practices required to maintain reliable AI systems after deployment.

Communicate business impact: Define initiative-level performance indicators, report outcomes to leadership, and connect technical decisions to measurable business value and long-term platform sustainability.

Advise clients and stakeholders: Build trusted relationships with client executives, communicate AI capabilities and limitations clearly, manage expectations, and guide decisions on scope, timelines, and technical trade-offs.

Collaborate with technology partners: Work with AWS solution architects and account teams to align on technical approaches, service selection, and go-to-market strategies for enterprise AI solutions.

Establish product leadership standards: Develop reusable product delivery practices, agent design patterns, evaluation methodologies, deployment playbooks, and knowledge-transfer materials.

Mentor and influence: Support the development of other AI product managers, strengthen product judgment across teams, monitor emerging AI trends, and contribute to proposals and business development initiatives.

Requirements

Professional experience: At least 8 years of experience in product management, forward deployment, solutions engineering, or a related discipline, including demonstrated success taking AI products from prototype to production at enterprise scale.

AI and agentic systems expertise: Strong practical knowledge of large language models, retrieval-augmented generation (RAG), fine-tuning, prompt engineering, context engineering, and AI evaluation methodologies, with hands-on experience building or delivering agentic systems.

Technical fluency: Ability to read and engage deeply with Python, JavaScript, or TypeScript code, assess technical architectures, and make informed product decisions within complex engineering constraints.

AWS ecosystem knowledge: Product-level expertise across AWS AI services and technologies, such as Amazon Bedrock, AgentCore, SageMaker, Strands, Kendra, OpenSearch, Lambda, and Step Functions.

Prototyping capabilities: Experience using AI-assisted development tools, such as Claude, Claude Code, Cursor, or GitHub Copilot, to test hypotheses, validate concepts, and reduce product delivery risks.

Evaluation and production expertise: Demonstrated experience designing and owning evaluation frameworks for production AI systems, including quality, reliability, safety, cost, and operational performance.

Strategic product judgment: Ability to identify high-value user and business problems, prioritize effectively with incomplete information, and balance immediate delivery requirements with long-term scalability.

Executive communication: Exceptional written and verbal communication skills, including the ability to produce clear product requirements documents, technical specifications, decision logs, and executive-level recommendations.

Stakeholder management: Proven ability to build alignment with directors, vice presidents, C-level executives, engineering leaders, and other cross-functional stakeholders on product direction and delivery decisions.

Adaptability and ownership: Comfortable navigating ambiguity, rapidly evolving technologies, and demanding delivery timelines while maintaining high standards of quality, accountability, and collaboration.

Additional advantages: A background in software engineering, technology consulting, or agency delivery; experience in financial services, healthcare, or life sciences; and familiarity with enterprise deployments involving open-source LLMs or multimodal AI.

Preferred qualifications: Experience contributing to business development or AI practice growth, a strong professional presence in the AI product community, or deep knowledge of industries such as retail, education, energy, financial services, healthcare, and manufacturing.

Benefits

Competitive compensation: Annual salary range of CAD 160,000–200,000, depending on relevant qualifications, experience, location, and other job-related factors.

Healthcare coverage: Medical, dental, and vision insurance for eligible employees.

Retirement benefits: Access to a 401(k) retirement savings plan for eligible employees.

Paid time off: Paid leave to support personal time, rest, and work-life balance.

Remote-friendly work environment: Opportunity to work remotely from Canada.

Professional development: Exposure to enterprise-scale AI initiatives, emerging technologies, and complex technical challenges across multiple industries.

Meaningful ownership: Lead high-impact AI product initiatives from initial discovery through production deployment, with opportunities to influence product strategy and delivery standards.

Collaborative expertise: Work alongside experienced product, design, engineering, and consulting professionals in a team-oriented environment.

Inclusive workplace: Equal employment opportunities and a commitment to fair, accessible hiring practices.

Hiring considerations: Employment may be conditional upon a background check in accordance with applicable legislation. Additional client-site travel may be required, including travel to the United States.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether? 

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