16 hours ago
Toronto, CanadaSenior
Responsibilities
- Translate AI policies, standards, and risk requirements into policy-as-code patterns, automated checks, deployment gates, configuration rules, runtime guardrails, and acceptance criteria.
- Define evidence requirements and validate that AI governance controls are implemented and remain effective.
- Partner with MLOps, platform engineering, architecture, cybersecurity, risk, and AI delivery teams across the AI lifecycle.
- Design reusable reference implementations and control patterns for lifecycle management, access governance, traceability, evaluation, and monitoring.
- Design and validate guardrails for large language model applications and AI agents, including identity, tool access, permissions, approvals, memory, connectivity, execution boundaries, and action traceability.
- Prototype control-assurance approaches using control configurations, test results, approvals, and operational telemetry.
- Review AI solution designs, identify control gaps, recommend mitigations, and define monitoring, material-change, exception, remediation, and escalation criteria.
Requirements
- Hands-on engineering experience in software engineering, machine learning engineering, MLOps, or AI platform engineering, including production systems.
- Strong programming skills, preferably in Python, with practical experience using APIs, Git, automated testing, and CI/CD pipelines.
- Understanding of the AI and machine learning lifecycle, including data preparation, evaluation, deployment, versioning, monitoring, and change management.
- Ability to translate policy and risk requirements into technical designs, implementation tasks, and testable acceptance criteria.
- Experience implementing automated controls such as policy as code, deployment gates, access restrictions, configuration validation, or infrastructure as code.
- Degree or diploma in computer science, engineering, data science, or a related field.
- Experience with Microsoft Azure, Databricks, large language model applications, retrieval-augmented generation, AI agents, AI evaluation frameworks, adversarial testing, AI security tooling, or regulated environments is an asset.
- Ability to work effectively with engineering and platform teams and adapt to evolving AI technologies.
Benefits
- Hybrid work arrangement with 1–3 days per week on-site and remaining days remote.
- Regular employment with 37.5 weekly hours in Toronto.
- Competitive salary and incentive pay.
- Banking benefits, a benefits program, defined benefit pension plan, employee share purchase plan, vacation offering, wellbeing support, and points-based recognition program.
- Paid Purpose Day for personal growth and development.
- Accessible candidate experience and accommodation support.
About CIBC
CIBC (Canadian Imperial Bank of Commerce) provides retail, business, wealth management, and capital markets services to individuals, small and mid-sized businesses, and institutional clients in Canada and the U.S. It earns revenue from interest income, fees, and trading and advisory services, and offers digital and branch banking, mortgages, credit cards, and investment products. Founded in 1961 and headquartered in Toronto, it is one of Canada’s Big Five banks and is listed on the TSX and NYSE (CM).