Program Manager for Applied AI/ML in Utilities Industry

We are looking for an experienced Program Manager to serve as a technical product/project manager for complex, multi-disciplinary technology engagements. This role sits at the intersection of business strategy and technical delivery, managing programs that bring together Data Architects, Machine Learning Engineers, data engineers, and data scientists to solve high-stakes problems for enterprise clients — including cloud data platform modernization (e.g., Microsoft Fabric, Azure, Snowflake) and applied AI/ML initiatives such as computer vision–based asset analytics. You will own end-to-end program planning and delivery: translating business objectives into actionable technical roadmaps, coordinating cross-functional teams, and ensuring engagements are delivered on time, on budget, and to a high technical standard. You will work directly with technical leads such as Data Architects and Lead Machine Learning Engineers to scope work, sequence dependencies, track progress, and remove blockers — while also managing client relationships and communicating status clearly to both technical and executive stakeholders. The ideal candidate is comfortable operating in a fast-paced, consulting-style environment, has enough technical fluency to engage credibly with architects and engineers on data and AI/ML projects, and knows how to translate complex technical work into clear plans, risks, and outcomes for business stakeholders.

Key Responsibilities
Delivery �
� Own end-to-end program and project planning for technical engagements spanning cloud data architecture, data engineering, and AI/ML delivery (e.g., computer vision, predictive analytics, MLOps). �
� Develop and maintain program roadmaps, schedules, budgets, resourcing plans, and risk/issue/dependency logs. �
� Break down complex technical initiatives — such as cloud data platform migrations (Microsoft Fabric, Azure, Snowflake) or ML model development and deployment pipelines — into actionable workstreams and sprints. �
� Coordinate closely with Data Architects, Lead Machine Learning Engineers, data engineers, and data scientists to sequence work, manage dependencies, and track delivery against milestones. �
� Apply Agile, hybrid, or Waterfall methodologies as appropriate to the engagement, and drive ceremonies such as sprint planning, stand-ups, and retrospectives. �
� Identify risks, blockers, and scope changes early, and drive mitigation plans in partnership with technical leads. Client & Stakeholder Management �
� Serve as a primary point of contact for client stakeholders, providing clear, timely status updates and managing expectations across technical and executive audiences. �
� Translate business requirements and priorities into technical scope, and translate technical constraints and trade-offs into business terms for client decision-makers. �
� Facilitate governance routines such as steering committees, status reviews, and executive briefings. �
� Support proposal development, scoping, and estimation for new or expanded engagements, partnering with technical leads on solutioning. Team Coordination �
� Coordinate staffing, onboarding, and day-to-day support for engagement teams, partnering with practice leads on resourcing decisions. �
� Facilitate collaboration across technical disciplines (architecture, data engineering, ML/AI, analytics) to ensure a cohesive, well-integrated solution. �
� Track and report on team capacity, utilization, and delivery health across concurrent workstreams �
� Contribute to reusable program management tools, templates, and best practices for technical delivery engagements.