Key Responsibilities
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.
