Job Description

AI Project / Delivery Manager - Atlanta, GA (Hybrid)

About the Role

We are seeking an experienced AI Project / Delivery Manager to lead the planning, execution, and successful delivery of artificial intelligence and machine learning initiatives across the enterprise.

This role bridges the gap between technical implementation and business impact —ensuring AI projects are delivered on time, within scope, and aligned with strategic objectives. The ideal candidate combines strong project management skills, a working knowledge of AI/ML concepts, and the ability to communicate effectively across technical and non-technical teams.

Key Responsibilities

• Project Ownership: Oversee the full lifecycle of AI projects—from initiation and business case development through deployment and post-implementation review.
• Cross-Functional Leadership: Partner with Data Science, Engineering, Product, and Business Units to define scope, success criteria, and resource requirements.
• Delivery Management: Lead agile delivery processes, manage sprint planning, backlogs, and daily standups for AI teams.
• Stakeholder Communication: Translate complex technical topics (LLMs, MLOps, NLP, computer vision) into clear business value and deliver progress updates to executives.
• Risk & Quality Management: Identify and mitigate delivery risks related to data, security, or performance. Ensure adherence to DevSecOps, governance, and compliance standards.
• Vendor & Platform Coordination: Manage third-party AI vendors, SaaS integrations (e.g., AWS SageMaker, Azure AI, OpenAI, or Vertex AI), and platform implementations.
• Metrics & Reporting: Establish KPIs to track model adoption, ROI, and delivery efficiency.
• Change Enablement: Support user adoption and training programs for AI-powered tools across departments.

Required Qualifications

• 5+ years of experience managing software, data, or AI-related projects.
• Proven experience leading Agile or Scrum delivery teams in a technical environment.
• Strong understanding of AI/ML fundamentals and lifecycle management (data prep, model training, deployment).
• Familiarity with cloud AI ecosystems (AWS SageMaker, Azure AI, or Google Vertex AI).
• Experience working with DevOps or MLOps frameworks.
• Demonstrated ability to manage multiple concurrent initiatives in complex enterprise environments.
• Excellent communication, stakeholder management, and executive presentation skills.

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