The Senior Project Manager in our Product Management Office (PMO) leads strategic, high-complexity projects and programs focused on artificial intelligence and machine learning initiatives. This role combines technical project leadership with business acumen, managing AI/ML implementations, data platform modernization, intelligent automation, and cross-functional digital transformation efforts. You'll orchestrate projects from concept through production deployment, ensuring AI solutions are delivered on time, within budget, and aligned with business value and ethical AI principles. As a trusted advisor to stakeholders and a mentor to project teams, you'll bridge technical, business, and consulting domains while championing agile methodologies and modern project management practices.KEY DUTIES/RESPONSIBILITIES
AI/ML Project Leadership & Delivery: Lead end-to-end delivery of complex AI and machine learning
projects, including model development initiatives, AI platform implementations, intelligent
automation solutions, and generative AI integrations. Drive projects through all lifecycle phases
using hybrid methodologies (Agile, Scrum, Waterfall, MLOps) tailored to AI/ML project needs.
Manage project scope, timeline, budget, and quality standards while navigating the unique
challenges of AI projects (model performance, data requirements, ethical
considerations).Coordinate dependencies across data engineering, ML engineering, data science,
and business stakeholder teams. Navigate the AI project lifecycle from use case identification
through model training, validation, deployment, and monitoring. (25%)
AI & Intelligent Automation Initiatives: Support implementation and optimization of AI/ML
platforms, including model training infrastructure, MLOps pipelines, feature stores, and model
monitoring systems. Coordinate cross-functional teams on projects involving generative AI
applications, natural language processing, computer vision, predictive analytics, and intelligent
process automation. Partner with data science, engineering, and business teams to deliver AI
solutions that drive measurable business outcomes and ROI. Manage relationships with AI
technology vendors, cloud providers (AWS, Azure, GCP), and AI consulting partners. Ensure
responsible AI practices including bias detection, explainability, data privacy, and governance
frameworks. (20%)
Stakeholder Communication & Collaboration: Serve as primary point of contact for AI project
teams, business owners, and executive sponsors. Deliver clear, concise communications including
status reports, executive dashboards, and risk assessments tailored for both technical and non-
technical audiences. Translate complex AI concepts and project progress into business value
language for leadership. Facilitate stakeholder alignment through sprint reviews, model review
sessions, steering committee meetings, and AI governance forums. Present project updates, model
performance metrics, and recommendations to leadership using data visualization and storytelling
techniques. (20%)
Team Coordination & Resource Management: Coordinate distributed, cross-functional teams
including data scientists, ML engineers, data engineers, software developers, UX designers, and
business analysts. Manage daily standups, sprint planning, model review sessions, retrospectives,
and other agile ceremonies. Monitor team velocity, sprint burndown, model development milestones, and progress against OKRs. Request and allocate specialized AI/ML resources based on
skill requirements and project priorities. Navigate resource constraints in competitive AI talent
markets. (15%)
Change Management & AI Adoption: Partner with business units to ensure smooth
implementation of AI solutions and intelligent automation. Develop and execute change
management plans addressing AI literacy, training, documentation, and user adoption. Validate
that AI capabilities are adopted, monitored, and that governance controls and feedback loops are
established. Review AI deliverables to ensure alignment with acceptance criteria, model
performance benchmarks, and business objectives. Address organizational change resistance and
AI anxiety through education and transparent communication. (10%)
Mentorship & Knowledge Sharing: Mentor junior and mid-level project managers on
methodologies, tools, and AI project best practices. Stay current on emerging trends in project
management, agile practices, AI technologies, and responsible AI frameworks. Contribute to PMO
process improvements and development of AI-specific templates, frameworks, and lessons
learned. Provide input to performance reviews for project team members. Build organizational AI
literacy through knowledge sharing and documentation. (5%)
Risk & Change Management: Identify, assess, and mitigate AI-specific project risks including data
quality issues, model performance degradation, ethical concerns, and regulatory compliance.
Evaluate scope changes and their impact on timeline, budget, resources, and model requirements.
Present change requests and recommendations to leadership with supporting analysis and impact
assessments. Maintain RAID logs (Risks, Assumptions, Issues, Dependencies) with AI-specific
considerations and escalate as needed. Monitor and address AI governance, security, and
compliance requirements. (5%)
