AI Assist Project Manager

Lockheed Martin•Sunnyvale, CA

1d•Onsite

About The Position

The Senior AI Project Manager is a high-autonomy, technically grounded individual contributor-leading role responsible for driving the end-to-end delivery of complex, multi-disciplinary artificial intelligence business development use-cases and initiatives. Operating with minimal oversight, this individual owns the full project lifecycle, from discovery and scoping through deployment and continuous improvement, while simultaneously advancing organizational AI maturity, governance practices, and cross-functional alignment. The ideal candidate brings hands-on experience in software development or AI/ML development, has transitioned into or alongside project and program management, and is equally fluent in technical architecture discussions and executive stakeholder communications. This person thrives in ambiguous, fast-moving environments, proactively identifies risk, and continuously improves delivery frameworks, ideally, using Agile, Six Sigma, or data-driven retrospective practice.

Requirements

10+ years of combined experience in software/AI development and technical project or program management, with at least 5 years dedicated to managing complex technology initiatives independently.
Demonstrated hands-on background in software engineering, data science, ML engineering, or a closely related technical discipline—capable of reading code, reviewing architecture diagrams, and engaging credibly with engineers on implementation tradeoffs.
Proven command of Agile methodologies (Scrum, Kanban, SAFe) with demonstrated ability to adapt frameworks to research-intensive and experimental AI workstreams.
Proficiency with modern project management and collaboration tooling (e.g., Jira, Confluence, Azure DevOps, GitHub Projects, Smartsheet) and data visualization platforms (e.g., Tableau, Power BI).
Bachelor’s degree in computer science, Engineering, Information Systems, or a related discipline, or equivalent combination of education and professional experience.
Ability to acquire and retain a TS/SCI clearance

Nice To Haves

Active TS/SCI clearance.
Master's degree or advanced certification in AI/ML, Computer Science, Data Engineering, or a related field.
PMP, PgMP, or PMI-ACP certification; Six Sigma Green Belt or Black Belt.
Direct experience managing AI/ML model development and deployment pipelines in production cloud environments (AWS, Azure, GCP).
Familiarity with MLOps practices and tooling (e.g., MLflow, Kubeflow, SageMaker, Vertex AI) and their integration into Agile delivery workflows.
Experience in defense, aerospace, intelligence, or other regulated industries where AI governance and security requirements are paramount.
Track record of delivering AI programs exceeding $5M in budget or spanning multiple geographically distributed teams.
Experience standing up or scaling an AI Center of Excellence (CoE), PMO, or delivery framework from inception.
Demonstrated contributions to AI ethics, responsible AI practices, or AI policy within an organizational context.
Exceptional written and verbal communication skills with a demonstrated ability to synthesize technical complexity into clear, concise executive narratives.
Practical Six Sigma or Lean experience applied to technology delivery processes; ability to conduct structured root cause analysis and drive measurable process improvement.

Responsibilities

Serve as the primary accountability point for AI Assist for Capture project status across technical teams, business stakeholders, and executive sponsors, adapting communication depth and style to each audience while maintaining full fidelity to program health.
Own the complete lifecycle of business development AI/ML projects from initiation and requirements definition through delivery, hypercare, and lessons-learned retrospectives, operating independently with authority to make scoping and prioritization decisions.
Define and maintain comprehensive project artifacts, charters, WBS, risk registers, resource plans, and executive dashboards, tailored for technical AI initiatives.
Establish and enforce clear milestones, success criteria, and acceptance frameworks, ensuring all AI project outputs meet defined quality, performance, and compliance standards before release.
Apply earned value management (EVM), critical path analysis, and schedule compression techniques to maintain delivery velocity against aggressive timelines.
Mentor junior project managers and technical leads, champion Agile and continuous improvement practices across delivery teams, and evaluate AI tooling and vendor solutions—presenting data-backed findings and recommendations to leadership.