Program & Delivery Management
Own and manage end-to-end delivery of ML/AI initiatives including planning, execution, monitoring, and release.
Define project scope, timelines, milestones, and deliverables in collaboration with business stakeholders and technical teams.
Prepare effort estimations, sprint planning, and delivery roadmaps to ensure predictable execution.
Ensure alignment with project objectives, business goals, and client expectations .
Agile & Execution Management
Lead Agile ceremonies including sprint planning, backlog grooming, stand-ups, and retrospectives.
Track and monitor delivery metrics such as velocity, burn-up/burn-down charts, release readiness, and productivity.
Identify and resolve delivery bottlenecks, ensuring smooth collaboration between teams.
Drive continuous improvement in Agile practices and delivery frameworks .
Stakeholder & Client Management
Act as the primary point of contact for internal and external stakeholders.
Provide regular status updates, reporting, and executive-level communication regarding program progress.
Manage stakeholder expectations and ensure transparency around risks, dependencies, and timelines .
Risk, Issue & Dependency Management
Proactively identify delivery risks, dependencies, and blockers .
Implement mitigation plans and ensure timely resolution of issues.
Maintain clear risk registers and escalation mechanisms .
Financial & Resource Management
Manage budgeting, cost tracking, and financial governance for projects.
Perform resource planning and allocation across multiple teams.
Coordinate with vendors, partners, and internal teams to ensure optimal resource utilization.
Contract & Governance
Ensure adherence to contractual agreements, SOWs, SLAs, and delivery commitments .
Maintain compliance with internal governance, quality standards, and delivery frameworks.
ML / AI Program Understanding
Demonstrate working knowledge of the Machine Learning lifecycle, including data preparation, model development, evaluation, deployment, and monitoring.
Understand MLOps practices such as CI/CD pipelines for ML models, model versioning, and monitoring.
Ensure proper data governance, security, and compliance practices in AI/ML initiatives.
Required Skills & Competencies
Strong experience in program or delivery management for data, ML, or AI-driven projects .
Deep understanding of Agile/Scrum delivery frameworks .
Experience managing cross-functional teams including data scientists, ML engineers, and data engineers .
Knowledge of ML lifecycle, MLOps practices, and data governance frameworks .
Strong ability to track metrics, analyze delivery performance, and drive improvements .
Excellent stakeholder communication, presentation, and reporting skills .
Proven experience in risk management, problem solving, and team unblocking .
Ability to work in fast-paced, complex program environments .
Preferred Qualifications
Experience delivering AI/ML platforms, analytics, or data engineering programs .
Certifications such as PMP, Scrum Master, SAFe, or Agile certifications .
Familiarity with cloud platforms (AWS, Azure, GCP) and data platforms .
Exposure to MLOps tools and modern data stack technologies .