Role: Senior Project Manager, Artificial Intelligence

Project Planning & Execution:

• Lead end-to end AI project delivery — from requirements gathering, feasibility analysis, and design to implementation and post-deployment optimization.
• Define clear project scope, timelines, milestones, and deliverables in alignment with business objectives.
• Manage crossfunctional teams including data scientists, ML engineers, software developers, and business analysts.
• Oversee the integration of AI solutions into existing systems or products.

Stakeholder Management:

• Act as the main liaison between technical teams and business stakeholders.
• Communicate project status, risks, and dependencies to senior management and clients.
• Translate technical outputs into business insights and actionable strategies.

Technical & Strategic Alignment:

• Work closely with AI architects and data teams to ensure data availability, quality, and governance.
• Support model lifecycle management — including model training, validation, deployment, and monitoring.
• Identify opportunities for applying AI/ML to automate processes, improve decision-making, or enhance customer experience.
• Ensure compliance with AI ethics, data privacy, and regulatory standards (e.g., GDPR, CCPA).

Governance & Reporting:

• Develop project documentation including charters, roadmaps, SOWs, and post-implementation reports.
• Track KPIs and project performance metrics to evaluate impact and return on investment (ROI).
• Manage project budgets, resources, and vendor relationships.

Project Management: PMP (Project Management Professional) certification is highly required.

Agile Methodologies: Certified ScrumMaster (CSM), SAFe Agilist (SA), or similar Agile certification.

AI/ML (Beneficial): Certifications in AI/ML from major cloud platforms (e.g., AWS Certified Machine Learning – Specialty, Azure AI Engineer Associate) are a significant advantage.