Technical Program Manager & MLOps & Data Governance

Work Mode: Hybrid (2 days per week in-person at Toronto office preferred)

Skills required:

• 10–12 years in technical program/project management with at least 3–5 years in data platforms and AI/ML operations.
• Strong understanding of data architectures (lake/lakehouse, warehouse, streaming), data governance, and MLOps/ModelOps concepts.
• MLOps/AI: Azure ML, SageMaker, Vertex AI; MLflow, model registry, feature stores, drift/fairness/explainability tools.
• Orchestration and CI/CD: Airflow, Prefect, dbt; GitHub Actions/Azure DevOps/Jenkins; Terraform/Bicep/CloudFormation.
• Cloud & Data: Azure (Synapse, Fabric), AWS (S3/Glue/Redshift), GCP (BigQuery/Dataflow), Databricks, Snowflake.
• Proven experience embedding security/privacy-by-design and RAI principles into delivery and ops.
• Excellent stakeholder management, vendor management, and executive communication skills.

Roles and responsibilities

• Program Delivery Leadership
• Own end-to-end delivery of data platform and AI/ML operational initiatives discovery design implementation hypercare steady-state operations.
• Maintain multi-quarter roadmap, backlog and release trains (Scrum, Kanban, SAFe), run standups, PI planning, demos and retros.
• Manage dependencies across data ingestion, storage processing, cataloging, lineage, access, MLOps pipelines and app integrations.
• Orchestrate cross-functional squads.
• Data Engineering, Platform SRE, Security, Risk, Legal and Business to deliver secure, governed and compliant data capabilities and AI services at scale.
• Own roadmaps, delivery governance, risk controls, release management and post-production reliability for data, AI workloads and ensuring Responsible AI principles are codified into day-to-day operations.
• Platform Technical Ownership
• Partner with Platform Engineering
• SRE to evolve the data platform reference architecture.
• Drive integration and operationalization of MLOps and Model Ops practices.
• Oversee environment strategy (dev test stage prod), IaC-driven provisioning, cost guardrails and performance SLAs.
• Embed Responsible AI guardrails into SDLC and runtime model cards, fairness bias checks, explainability, human-in-the-loop, monitoring drift and incident response.
• Operationalize data governance meta data catalog, lineage, PII classification, DLP, RBAC (Role-Based Access Control), ABAC (Attribute-Based Access Control), data quality SLAs, retention deletion schedules.
• Align with privacy, security and regulatory frameworks (e.g. privacy laws, model risk management and AI assurance frameworks).
• Risk and Compliance Controls
• Maintain risk register, control library, audit trail, approvals and evidence for releases and model lifecycle events.
• Run change advisory (CAB) workflows for platform and model changes ensure traceability from requirements to deployment and monitoring.
• Translate business outcomes into measurable platform and AI service capabilities, SLIs and SLOs.
• Provide executive-level status (OKRs, KPIs, burn-up down, RAID, budget vs. actuals)

Certifications (nice-to-have):