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Responsibilities:
Manage and coordinate end-to-end delivery of AI/ML, Generative AI, Data Science, MLOps, or AI Platform projects using Agile methodologies.
Work closely with Product Managers, Product Owners, Business Stakeholders, Architects, Data Scientists, AI/ML Engineers, Data Engineers, and other technical teams to ensure successful project delivery.
Plan project scope, timelines, milestones, resources, dependencies, and deliverables.
Drive Agile/Scrum ceremonies such as Sprint Planning, Daily Stand-ups, Backlog Refinement, Sprint Reviews, and Retrospectives.
Track project progress against agreed timelines, milestones, scope, and quality expectations.
Identify, track, and proactively manage project risks, issues, assumptions, and dependencies.
Coordinate activities across multiple technical and business teams to ensure smooth execution of projects.
Facilitate communication between technical teams and business stakeholders and ensure clear understanding of requirements and priorities.
Monitor project deliverables and ensure that teams remain aligned with business objectives.
Support prioritization and execution of AI/Data capabilities, platforms, products, and solutions.
Understand project-level technical dependencies related to AI/ML models, GenAI solutions, data pipelines, MLOps, AI platforms, and cloud/data environments sufficiently to coordinate delivery effectively.
Required Experience:
5–10 years of experience in project/program management, technology delivery, or a related role.
Strong experience working in Agile/Scrum project environments.
Demonstrated experience managing or supporting Data, AI/ML, Generative AI, Data Science, MLOps, or AI Platform projects.
Experience delivering or supporting at least one AI capability, AI-powered product, platform, or solution.
Ability to work effectively with highly technical teams and understand the overall lifecycle of AI/ML and Data projects.
Strong stakeholder management, communication, planning, prioritization, and problem-solving skills.
Experience managing multiple workstreams, technical dependencies, risks, and delivery milestone
Work arrangement
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