We are looking for an experienced US Project Manager Data Platforms to lead and deliver enterprise AI/ML platform initiatives, including SageMaker Migration, ModelOps, AWS Bedrock, Azure AI Foundry, and production onboarding. The ideal candidate will have strong project and program management experience, with the ability to manage end-to-end delivery from planning through deployment while ensuring timelines, budgets, scope, and quality objectives are met.
The candidate will be responsible for coordinating cross-functional teams across Engineering, Architecture, Cloud, Security, IAM, Data Engineering, Operations, Product, vendors, and business stakeholders. The role requires expertise in project planning, milestone tracking, risk management, governance, stakeholder communication, SIT/UAT testing, release readiness, deployment coordination, and Go/No-Go decision-making. Experience managing enterprise data platforms, Big Data, analytics, and AI/ML initiatives is highly desirable.
Required Skills:
- Strong experience managing enterprise data platform and technology projects.
- Experience leading AI/ML platform initiatives, including SageMaker Migration, ModelOps, AWS Bedrock, and Azure AI Foundry.
- Proven ability to manage end-to-end project and program execution from planning through production deployment.
- Strong expertise in project planning, scheduling, budgeting, resource allocation, and performance management.
- Experience managing project scope, costs, timelines, dependencies, milestones, and contractual deliverables.
- Experience coordinating cross-functional teams across Engineering, Architecture, Cloud, Security, IAM, Data Engineering, Operations, Product, and business functions.
- Strong understanding of Big Data platforms, data engineering, analytics, and enterprise data solutions.
- Experience with SIT/UAT testing, release planning, deployment coordination, and production onboarding.
- Ability to manage release readiness assessments and facilitate Go/No-Go decisions.
- Strong experience in project governance, risk management, and issue resolution.
- Expertise in maintaining RAID logs (Risks, Assumptions, Issues, and Dependencies).
- Experience tracking project KPIs, milestones, deliverables, and overall project performance.
- Ability to identify project risks, manage dependencies, remove blockers, and escalate critical issues.
- Strong executive communication, stakeholder management, and status reporting skills.
- Experience managing vendors, third-party partners, and distributed project teams.
- Knowledge of Agile, Waterfall, and hybrid project management methodologies.
- Strong analytical, problem-solving, decision-making, and organizational skills.
- Ability to manage multiple workstreams and competing priorities in a fast-paced enterprise environment.
- Excellent verbal and written communication skills, with the ability to engage technical and business stakeholders.
- Financial Services / Banking experience is preferred.
- Experience delivering enterprise-scale AI/ML, cloud, data engineering, or analytics transformation programs is highly desirable.
Preferred Qualifications:
- Experience with AWS cloud platforms and AI/ML services, particularly Amazon SageMaker and AWS Bedrock.
- Familiarity with Microsoft Azure AI Foundry and enterprise AI platform solutions.
- Experience with ModelOps, MLOps, AI model deployment, and production operationalization.
- Experience supporting enterprise cloud migration and modernization initiatives.
- Strong understanding of enterprise architecture, cloud security, IAM, and data governance.
- Experience working in IBM Consulting or large enterprise consulting environments is an advantage.
Key Responsibilities:
- Lead and deliver AI/ML and data platform programs, ensuring successful execution against agreed timelines, scope, budget, and quality standards.
- Develop and maintain comprehensive project plans, schedules, milestones, resource plans, and delivery roadmaps.
- Coordinate cross-functional teams and stakeholders to ensure alignment across technical and business workstreams.
- Monitor project progress, track KPIs, manage dependencies, and provide regular status updates to leadership.
- Manage project risks, issues, assumptions, and dependencies, implementing mitigation plans and escalating critical blockers.
- Coordinate SIT/UAT activities, release readiness, production onboarding, deployment schedules, and Go/No-Go decisions.
- Drive governance processes, stakeholder reviews, executive reporting, and change control.
- Ensure successful delivery of cloud and AI/ML platform initiatives, including migration, ModelOps, and production deployment.
- Facilitate communication between engineering teams, architects, business stakeholders, vendors, and senior leadership.
- Ensure project deliverables meet organizational standards, contractual requirements, and business objectives.