AI&D Delivery Manager

Overview

Delivery Manager

AI & Data Practice

data engineering, data migration, platform modernization, visualization, data science, machine learning, and Generative AI

hybrid and cloud platforms

Responsibilities

Key Responsibilities

• Program & Project Delivery
• Lead the end-to-end delivery of AI & Data programs across hybrid and cloud environments.
• Manage multiple teams (5-50 members) across data engineering, ML/AI, and visualization streams.
• Ensure adherence to project timelines, budgets, and quality standards.
• Stakeholder Management
• Serve as the primary point of contact for customers, ensuring clear communication and alignment.
• Work closely with business sponsors, product owners, and technical leads to define scope, outcomes, and priorities.
• Build strong relationships with customer stakeholders and executive sponsors.
• Solution Oversight
• Oversee design, migration, and implementation of solutions across data pipelines, platforms, and ML/AI models.
• Guide decisions on modernization, refactoring, and cloud adoption strategies.
• Support solution architects and technical leads in aligning delivery to business outcomes.
• Financial & Governance Management
• Own project financials, including budgeting, forecasting, and cost tracking.
• Ensure compliance with delivery governance, risk management, and reporting standards.
• Track and report on project KPIs, success metrics, and ROI.
• Team Leadership
• Lead distributed teams across engineering, data science, and platform operations.
• Provide guidance, mentorship, and conflict resolution within project teams.
• Foster a culture of accountability, innovation, and continuous improvement.
• Cloud & Data Lifecycle Knowledge
• Apply deep understanding of data lifecycle management, data science, ML/AI workflows, and Generative AI use cases.
• Leverage experience with at least one major cloud platform (GCP preferred; AWS or Azure beneficial).
• Stay current with evolving AI/ML and data technology trends to guide clients in adopting innovative solutions
  

Requirements

Required Qualifications

• 12-15 years of IT experience, with at least 6-8 years in managing data and ML/AI lifecycle projects.
• Proven experience in program/project management across data engineering, migration, cloud modernization, and advanced analytics initiatives.
• Strong understanding of data lifecycle management, data science, machine learning, and emerging Generative AI applications.
• Hands-on exposure to at least one cloud platform (GCP preferred; Azure/AWS beneficial).
• Solid expertise in project financial management, governance, and reporting.
• Strong background in Agile and hybrid delivery models.
• Excellent communication, leadership, and stakeholder management skills.
  

Preferred Qualifications

• Project Management certifications (PMP, PRINCE2, PMI-ACP, or equivalent).
• Cloud certifications (Google Cloud Professional Data Engineer / Architect, AWS/Azure equivalents).
• Experience working with global teams and large enterprise customers.
• Prior experience in a Global System Integrator (GSI) environment is highly desirable.