AI Project Lead – Hybrid Delivery & Governance

Cognizant

Philadelphia (Philadelphia County)

On-site

USD 70,000 - 117,000

Full time

3 days ago

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Benefits offered by this job

Medical/Dental/Vision/Life Insurance

Paid holidays + PTO

401(k) plan and contributions

Long-term/Short-term Disability

Paid Parental Leave

Employee Stock Purchase Plan

Job summary

Cognizant is seeking an AI Project Manager to lead end-to-end delivery of AI, ML, and data initiatives from planning through deployment in a largely onsite role in Philadelphia. You will collaborate with data scientists, ML engineers, developers, business stakeholders, vendors, and IT teams to ensure responsible, scalable AI solutions aligned with business objectives.

You will define scope, milestones, and delivery plans, lead Agile ceremonies, translate requirements into user stories, manage

Qualifications

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
Five or more years of project management experience, including at least two years leading Agile or Scrum projects.
Experience managing AI, machine learning, data analytics, or other data-driven initiatives.
Strong understanding of the AI and machine learning project lifecycle, including data readiness, model development, deployment, and operational support.
Strong communication, stakeholder management, risk management, and cross-functional team coordination skills.
Proficiency with project delivery and collaboration tools such as Jira, Microsoft Project, Azure DevOps, or Confluence.

Responsibilities

Define project scope, objectives, milestones, resource requirements, and delivery plans for AI, ML, data initiatives, including data readiness, model development, deployment.
Lead Agile delivery activities, including sprint planning, backlog refinement, daily stand-ups, reviews, and retrospectives, while tracking progress through Jira, Microsoft Project, and Azure DevOps.
Translate business requirements into user stories, acceptance criteria, and measurable success metrics, including model performance, user adoption, and return on investment.
Identify and manage risks involving data quality, model bias, security, privacy, regulatory compliance, and responsible AI governance while maintaining audit-ready documentation.
Communicate project status, priorities, risks, and outcomes through reports, dashboards, demonstrations, and stakeholder meetings.
Coordinate team capacity, vendor contributions, and operational readiness to ensure AI solutions are maintainable, scalable, and aligned with enterprise standards.