Staff AI Operations Lead

Accountabilities

• Partner with department leaders to identify high-value business workflows that can benefit from AI automation, developing a deep understanding of existing processes, challenges, and opportunities.

• Design, build, deploy, and maintain end-to-end AI-powered workflows that improve operational efficiency, consistency, quality, and speed.

• Define measurable success criteria before implementation, including cycle time, output quality, cost, and risk, and track results transparently after deployment.

• Develop production-ready AI systems with robust evaluation frameworks, appropriate human review mechanisms, ongoing monitoring, and clear performance indicators.

• Improve adoption through user research, practical documentation, stakeholder engagement, and structured rollout plans.

• Establish reusable patterns and shared components for model access, orchestration, AI agents, prompt and tool management, evaluation, logging, and cost control.

• Build on existing data infrastructure and collaborate with Engineering to develop shared capabilities, identify dependencies, and coordinate implementation plans.

• Create templates, playbooks, safety checklists, and recommended implementation patterns that help teams adopt AI consistently and effectively.

• Lead enablement initiatives, including office hours, demonstrations, community-of-practice sessions, and tailored learning materials for business operations, go-to-market teams, and leadership.

• Help employees develop sound judgment around AI adoption, including when to use AI, when alternative approaches are more appropriate, and how to evaluate outcomes.

• Partner with Security, IT, Legal, and Data teams to establish appropriate access controls, data-handling practices, vendor-risk assessments, and governance policies.

• Apply data classification standards to AI use cases, ensuring sensitive information is handled appropriately and required audit trails are maintained throughout AI workflows.

• Continuously assess emerging AI capabilities and refine solutions based on operational evidence, user feedback, performance data, and evolving business needs.

Requirements

• Extensive experience delivering production-ready systems that automate or improve real business workflows, with end-to-end ownership of solutions used regularly by nontechnical teams.

• Hands-on expertise with modern AI technologies, including large language models (LLMs), retrieval techniques, tool use, AI agents, evaluation frameworks, and guardrails.

• Strong ability to translate business problems into practical technical solutions, with the judgment to distinguish where AI adds value from where simpler approaches are more effective.

• Demonstrated success leading cross-functional initiatives without relying on formal authority, bringing structure, clear decisions, and momentum to complex projects.

• Experience working closely with business stakeholders to understand operational processes, identify inefficiencies, and deliver measurable improvements.

• Familiarity with security, privacy, and compliance considerations in enterprise environments, including data classification, retention policies, access controls, and vendor risk.

• Strong communication and teaching skills, including the ability to explain complex technical concepts clearly, create useful documentation, and adapt guidance to different audiences.

• A continuous-learning mindset, with the ability to evaluate emerging AI technologies, challenge assumptions, and adjust implementation decisions based on evidence.

• Ability to work independently while collaborating effectively with Data, Engineering, Security, IT, Legal, and business teams.

Nice-to-have qualifications:

• Proficiency in SQL and familiarity with modern data platforms, including dbt, cloud data warehouses, and business intelligence tools.

• Experience with workflow automation and enterprise systems such as CRM platforms, ticketing tools, case management systems, and knowledge bases.

• Familiarity with AI observability practices, including quality monitoring, evaluation pipelines, cost tracking, and latency analysis.

• Experience managing organizational change and supporting AI adoption across distributed, cross-functional teams.

• Interest in external knowledge sharing through writing, speaking, podcasts, or professional communities.

Benefits

• Competitive base salary:CAD $310,000–$360,000, depending on experience and level.

• Equity participation:Generous equity opportunities through an employee-friendly stock program.

• Transparent compensation:A structured pay approach based on role level and experience.

• Unlimited paid time off:Flexibility to take time away from work and recharge.

• Remote-first culture:A distributed working environment that emphasizes results, autonomy, and accountability.

• Home office support:Stipends for home office equipment, coworking spaces, and internet expenses.

• Healthcare coverage:Comprehensive benefits for employees, with additional coverage available for eligible dependents.

• Paid parental leave:Up to 16 weeks of paid leave, regardless of the path to parenthood.

• Professional development:An annual allowance to support learning and career growth.

• Work authorization:Employment sponsorship and visa transfers are not currently available. Candidates must be legally authorized to work in Canada.

• Location eligibility:Hiring is currently limited to candidates residing in British Columbia, Ontario, or Quebec.

• Inclusive workplace:A commitment to diversity, equity, accessibility, and reasonable accommodations throughout the recruitment process.