Fully Remote - Senior Program Manager – AI Governance And Implementation

TEKsystems•Calgary, AB

6d•CA$100 - CA$120•Hybrid

About The Position

The Municipal Government Client of TEKsystems is seeking an experienced and strategic leader to lead a comprehensive AI Governance Review and Implementation program. This role will guide the evolution of the client's AI governance model to support the safe, transparent, and scalable adoption of AI technologies, including Microsoft Copilot and agentic AI capabilities. The Municipal Government Client of TEKsystems has implemented the enterprise AI Strategy and established its AI governance model since 2023. As the Senior Program Manager, you will collaborate across IT, business units, governance bodies, and external partners to assess, design, and implement effective AI governance structures, risk frameworks, and operating processes. The position focuses on strengthening consistency and maturity in the organization’s AI governance, ensuring alignment with public sector best practices, and enabling responsible AI adoption that builds public trust while delivering measurable improvements in service outcomes.

Requirements

Ten (10) years' IT project management experience (the majority of this experience should have been practiced in accordance with the generally recognized good practices identified in PMI’s PMBOK® Guide, in recent years).
At least four (4) of the ten (10) years must have been in an IT program management capacity in a complex organization that impacts multiple business units, departments, or the organization as a whole.

Nice To Haves

Minimum five (5) years leading medium to large technology initiatives focused on AI/data governance or enterprise AI/data programs, defined by scale, complexity, stakeholder impact, and risk.
Demonstrated experience delivering enterprise-scale AI, data, or digital transformation programs (ideally in public sector or regulated environments).
Proven ability to translate emerging AI capabilities (e.g., generative AI, Copilot, agentic AI) into structured programs with defined governance, controls, and measurable outcomes.
Experience working with governance bodies and driving cross-business decision-making, while balancing innovation with risk and alignment to organizational values and public expectations.
Strong understanding of AI/ML, generative AI, and enterprise AI platforms (preferably Microsoft ecosystem).
Solid knowledge of the AI/ML lifecycle (data, model development, validation, deployment, monitoring) and governance across each stage.
Familiarity with generative AI risks (hallucination, data leakage, bias) and mitigation approaches (prompt controls, human-in-the-loop, content filtering).
Experience with platforms such as Microsoft 365 Copilot, Azure AI, and low-code automation tools.
Ability to engage both technical and non-technical stakeholders to align use cases with platform capabilities.
Experience developing AI governance models, operating frameworks, and decision-rights structures.
Proven ability to design governance frameworks with clear accountability, decision rights, and escalation paths (e.g., RACI).
Experience formalizing governance bodies (advisory boards, working groups) with defined mandates and processes.
Ability to operationalize policies, standards, and procedures for AI intake, approval, and monitoring, and integrate them into existing enterprise governance (IT, risk, audit).
Experience with risk-based regulatory and compliance frameworks for AI.
Familiarity with risk classification approaches based on impact and sensitivity.
Experience applying frameworks such as Algorithmic Impact Assessments (AIA), NIST AI RMF, and OECD AI Principles.
Strong understanding of privacy, cybersecurity, legal, ethical, and human rights risks.
Ability to implement structured review processes (PIAs, ethical reviews, bias testing) within delivery pipelines.
Strong Senior leadership and stakeholder engagement skills.
Experience leading cross-functional teams across IT, legal, privacy, risk, HR, and business units.
Ability to build consensus among executives, technical teams, and policy stakeholders, and facilitate governance forums and decision-making sessions.
Demonstrated change leadership to drive adoption of responsible AI practices and cultural shifts.
Knowledge of public-sector compliance, privacy, and accountability requirements.
Familiarity with privacy legislation, records management, and transparency expectations.
Understanding of auditability, reporting, and public accountability (including Council reporting).
Experience working with oversight functions (legal, privacy, audit, risk) and managing public perception risks (e.g., employee concerns, union considerations).
Experience with enterprise platforms, particularly Microsoft ecosystem, is an asset.
Hands-on or program-level experience with Microsoft 365, Copilot, Azure AI, and Power Platform.
Understanding of how AI capabilities integrate into workflows and business processes.
Experience defining usage guardrails, acceptable use policies, and monitoring practices, and collaborating with vendors to align technical capabilities with governance requirements.
Relevant Microsoft and AI/ML certifications are considered an asset.

Responsibilities

Lead the refinement and implementation of the client's AI governance framework, including strengthening governance decision-making bodies.
Define and operationalize governance structures, including decision rights, RACI models, and escalation paths.
Align governance to leading public sector AI frameworks and standards.
Implement structured AI intake, risk classification, and tiered review processes.
Integrate privacy, legal, ethical, and human rights considerations across the AI lifecycle.
Establish escalation and risk mitigation mechanisms for high-risk AI systems.
Lead cross-functional program execution involving IT and corporate partners.
Facilitate governance collaboration and decision-making.
Provide executive-level reporting to Senior leadership.
Design AI transparency and reporting frameworks.
Enable auditability and independent oversight.
Strengthen public accountability and trust in AI adoption.
Establish scalable governance processes for enterprise AI adoption.
Drive ongoing improvement based on evolving technology and policy.