AI Technical Project Manager

Please find job description:

Role: AI Technical Project Manager

Location: Santa Clara, CA (5 Days ONSITE)

Job Type: Contract

MUST HAVE: Project Manager, GenAI/LLM, ML Programming, AI/ML, Data Science Experience

Job Description:

About the Role

• Lead end-to-end delivery of AI/ML programs, from ideation through deployment and post-production monitoring.
• Define program scope, goals, timelines, budgets, and success metrics.
• Coordinate cross-functional teams including data science, engineering, product, legal, security, and compliance.
• Develop and maintain detailed project plans, roadmaps, and risk registers.
• Track dependencies, milestones, and deliverables across multiple workstreams.
• Ensure AI solutions align with business objectives and technical architecture standards.
• Manage stakeholder communication and provide regular status updates to leadership.
• Identify and mitigate risks related to data quality, model performance, governance, and compliance.
• Drive adoption of responsible AI practices, including fairness, transparency, and explainability.
• Monitor production KPIs such as model accuracy, latency, drift, and ROI.
• Facilitate vendor management and coordination with external partners when needed.
• Support change management, user training, and rollout activities.

Required Qualifications:

• Bachelor s degree in Computer Science, Engineering, Data Science or a related field.
• 10+ years of program or project management experience.
• 3+ years managing AI, Machine Learning program (forecasting, NLP), Generative AI/LLM Program, or advanced analytics programs.
• Strong understanding of AI/ML lifecycle, including data preparation, model training, validation, deployment, and monitoring.
• Experience with Agile, Scrum, and Waterfall methodologies.
• Proficiency with project management tools such as Jira, Confluence, Microsoft Project, or Smartsheet.
• Strong communication, stakeholder management, and leadership skills.

Preferred Qualifications:

• PMP, PgMP, Scrum Master, or SAFe certification.
• Experience with cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud.
• Familiarity with AI frameworks such as TensorFlow, PyTorch, and OpenAI APIs.
• Knowledge of data governance, privacy regulations, and responsible AI principles