Director, Enterprise Machine Learning Frameworks & Operations

We’re building a relationship-oriented bank for the modern world. We need talented, passionate professionals who are dedicated to doing what’s right for our clients.

At CIBC, we embrace your strengths and your ambitions, so you are empowered at work. Our team members have what they need to make a meaningful impact and are truly valued for who they are and what they contribute.

To learn more about CIBC, please visit CIBC.com

What You’ll Be Doing

The Director, Enterprise Machine Learning Frameworks & Operations will lead the design, development, and scaling of enterprise-wide ML Operations (MLOps / LLMOps) capabilities. This role is pivotal in establishing AI at scale by driving operational efficiency, reliability, and compliance throughout the entire model development lifecycle (from experimentation to production deployment). You will shape the MLOps strategy and oversee the implementation of tooling, frameworks, and engineering standards that ensure secure, scalable, and repeatable delivery of AI/ML and LLM solutions across the bank. You will architect and build reusable platform capabilities, reference architectures, deployment patterns, and platform services that support both traditional machine learning and large language model use cases across multiple lines of business. Success will be measured by your ability to define and standardize technical activities, mature operational practices, and drive measurable business impact and stakeholder adoption. The ideal candidate possesses deep technical expertise in MLOps, hands-on engineering leadership, and strong stakeholder engagement skills to deliver on a strategic vision for enterprise-scale AI operations.

At CIBC we enable the work environment most optimal for you to thrive in your role. You’ll have the flexibility to manage your work activities within a hybrid work arrangement where you’ll spend 1–3 days per week on-site, while other days will be remote.

How You’ll Succeed

Technical Leadership and Contribution - Translate strategic capability roadmaps into actionable technical deliverables, including the design and implementation of CI/CD pipelines, end-to-end MLOps workflows, and automated ML pipelines for data ingestion, training, validation, and deployment. Ensure workflows are compliant with regulatory and enterprise governance requirements, including approval gates, automated monitoring and alerts, and audit-ready processes. Oversee the development of enterprise-grade, production-ready, and standardized MLOps capabilities to support scalable and reliable AI delivery across the organization. Set and communicate best practices and guidelines to ensure consistency and high-quality delivery. Drive the creation of reusable frameworks, platform services, engineering standards, and deployment patterns that accelerate AI delivery while reducing operational complexity and duplication across teams.
Stakeholder Management and Collaboration - Build strong relationships and collaborate with business partners, technology teams, and cross-functional stakeholders to gather requirements, prioritize initiatives, and align MLOps solutions with business objectives. Partner with AI governance, Compliance, and technology teams to ensure all solutions adhere to regulatory and internal standards. Participate in code reviews, identify updates to and the need for new documentation, and share best practices. Stay current with emerging AI/ML tools and techniques, actively contributing to a culture of experimentation and continuous improvement. Advise on the MLOps strategy and capability roadmap based on stakeholder feedback and emerging needs. Act as the primary point of contact for business and technical stakeholders regarding MLOps delivery.
MLOps/ LLMOps Development and Production Deployments– Lead the end to end model deployment pipeline management. Develop and sustain production-ready deployments (including support for APIs and open-source models). Oversee Retrieval-Augmented Generation (RAG) pipelines. Partner with AI and Enterprise technology teams to ensure alignment on reusable technology components across all use cases, incorporating compliance requirements early to avoid delays, and maintaining lineage and version tracking. Assess models to prevent performance degradation and establishing monitoring frameworks to analyze inference latency, optimize token usage, and detect potential hallucinations in real time
People & Culture - Champion a culture of innovation, collaboration, and continuous learning within the MLOps team and across the enterprise. Upskill and mentor cross-functional teams on MLOps tools, frameworks, and best practices. Exhibit strong influencing, negotiation, and conflict resolution skills, with the ability to align diverse stakeholders around common goals.

Who You Are

You can demonstrate 8+ years of experience in software engineering, platform engineering, data platforms, AI/ML engineering, or MLOps, with at least 2 years in a leadership role. Proven track record delivering scalable ML platforms in a highly regulated environment. It’s an asset if you have experience in financial services or other regulated industries. You have relevant knowledge and deep expertise with cloud-native architectures and technologies (e.g., Azure ML, Databricks, Kubernetes). Expertise with CI/CD, monitoring, model governance, and observability frameworks.
You have a degree in. Computer Science, Statistics, Engineering, or a related field. You have a strong software engineering and platform engineering foundation with experience building production-grade platforms, distributed systems, AI/ML services, developer platforms, or enterprise-scale automation frameworks.
Your influence makes a difference. You are comfortable operating as a hands-on technical leader who can balance strategic direction with deep technical discussions, architectural design, engineering trade-offs, and implementation guidance.
You’re driven by collective success. You ha