AI Project Manager

Project Management (PM) comprises project management for execution of cross-functional / geographical projects and programmes from initiation to completion. Covers determination of project / programme goals and support of business objectives and strategies. Ensures projects / programmes achieve their targets, keep their schedule, and maintain estimated cost, time, and quality within planned scope. Covers management of risks that affect the delivery of project outcomes.

• Lead the full project lifecycle for AI/ML and GenAI initiatives — scoping, planning, resourcing, execution, and closure — using Agile, Scrum, or hybrid methodologies.
• Manage cross-functional squads spanning data scientists, AI architects, platform engineers, and business SMEs.
• Partner with IT and DO process owners to identify AI use cases tied to measurable process KPIs (cycle time, cost-to-serve, automation rate, quality).
• Ensure alignment with Nokia's IT governance, architecture standards, security, and data-protection requirements.
• Act as the single point of accountability for sponsors, business owners, and IT leadership.
• Run steering committees, executive reviews, and demo cadences; communicate progress in business terms.
• Maintain the AI delivery portfolio view, dependencies, and capacity plan.
• Produce monthly/quarterly status, financials, benefits, and risk reports for leadership.

• 8+ years of project/program management experience, with at least 3 years delivering AI/ML, data, or automation projects.
• Proven track record managing initiatives within IT and/or Digital/Delivery Office environments (ITSM, DevOps, automation, employee experience, or shared services).
• Strong grasp of the AI/GenAI lifecycle
• Hands-on experience with Agile/Scrum and project tooling (Jira, Azure DevOps, MS Project, Confluence).
• PMP, PRINCE2, or SAFe certification; Scrum Master certification preferred.

• Excellent stakeholder management and written/verbal communication in English.

  Nice to Have:

• Experience with cloud AI platforms (Azure AI, AWS, GCP) and enterprise GenAI tooling (Copilot, OpenAI, LangChain, vector DBs).
• Familiarity with ITIL v4, ServiceNow, and modern DevOps toolchains.
• Background in large-enterprise IT transformation.
• Exposure to responsible-AI frameworks and data-privacy regulations (GDPR).