Technical Program Manager - Toronto - Astra North Infoteck Inc.

    Astra North Infoteck Inc.
    Astra North Infoteck Inc. Toronto

    2 days ago

    Description

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

    Skills required:

    · –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.

    · Data Governance: Purview, Collibra, Alation; data lineage, cataloging, DQ tooling.

    · Orchestration and CI/CD: Airflow, Prefect, dbt; GitHub Actions/Azure DevOps/Jenkins; Terraform/Bicep/CloudFormation.

    · Monitoring/Observability: Prometheus/Grafana, cloud-native monitors, logging, data quality monitors, model monitoring.

    · 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.

    · Responsible AI Data Governance.

    · 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.

    · Stakeholder Management Communication.

    · 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):

    · PMP/Prince2, CSM/SAFe, Azure/AWS/GCP data/AI, Databricks/Snowflake, Governance/Privacy.


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