Technical Program Manager - AI & Data Systems

Abnormal AI Abnormal AI · Vertical AI · United States · Remote · CIO AI Transformation

Technical Program Manager to lead delivery of high-impact programs across AI Platform and Data Systems teams, focusing on AI governance, data foundation for AI agents, AI outcomes analytics, and new agent/platform rollouts. The role involves defining processes, driving outcomes, and aligning stakeholders, with an emphasis on using AI tools to solve delivery challenges.

What you'd actually do

  1. Own program delivery for high-impact AI Platform and Data Systems initiatives, from initiation through completion — including scope definition, milestone planning, dependency and escalation management, and stakeholder alignment.
  2. Translate ambiguous mandates from IT leadership into structured, trackable programs with clear owners and success criteria.
  3. Serve as the coordination layer for high-impact, cross-functional programs — working in lockstep with engineering leaders and teams, ensuring dependencies are visible and delivery stays on track.
  4. Maintain executive-ready status reporting for AI Data Systems leadership and C-level stakeholders, ensuring no surprises and consistent confidence in program and portfolio health.
  5. Identify, escalate, and mitigate risks across programs before they impact delivery timelines or stakeholder trust.

Skills

Required

  • 3-4 years of program or project management experience leading complex, cross-functional technical initiatives
  • Demonstrated ability to take an early-stage idea — where the goal is clear but the path isn't — and build it into structured delivery with measurable outcomes.
  • Proven ability to move fast and drive velocity — keeping programs progressing through ambiguity and unknowns.
  • Track record of managing executive-level stakeholder relationships, including C-level communication, with poise and precision.
  • Strong experience working alongside technical team leads in AI, engineering, data, or platform contexts — you can follow the technical conversation, ask the right questions, and push back on sequencing when it matters.
  • Familiarity with AI tools and an automation instinct — you naturally ask "how can I use AI to do this?" and have applied it to improve how you work, whether personally or professionally.
  • Excellent command of project tracking and program management tools (Linear, Jira, ServiceNow, Asana, or equivalent).
  • Exceptional communicator across technical and non-technical audiences — written updates, verbal alignment, and executive readouts are all in your wheelhouse.
  • Demonstrated ability to drive accountability and alignment across teams without formal authority.
  • Bachelor's degree in Computer Engineering, Information Technology, Business Administration, Information Security or a related field

Nice to have

  • Hands-on experience using agentic AI tools (Claude Code, Codex, Cowork, or similar) to build skills, automations, or multi-step workflows — not just to use AI passively.
  • Familiarity with AI/ML platform delivery, data engineering workflows, data warehousing, or MLOps practices.
  • PMP, CSM, or SAFe certification.
  • Experience at a high-growth cybersecurity or SaaS company.
  • Experience in a remote-first, distributed team environment.
  • Exposure to cloud infrastructure (AWS, GCP, or Azure) and how it intersects with data and AI platform programs.
  • Experience with Agile/Scrum ceremonies and sprint-level delivery in addition to program-level ownership.

What the JD emphasized

  • high-impact AI Platform and Data Systems initiatives
  • AI governance
  • data foundation for AI agents
  • AI outcomes analytics
  • new agent and platform rollouts
  • turn them into structured, well-tracked programs
  • define processes where none exists
  • drive valuable outcomes
  • keep executive stakeholders aligned and ahead of surprises
  • intersection of engineering execution and leadership visibility
  • thrives in ambiguity
  • earns credibility through follow-through
  • instinctively reaches for AI to solve real delivery challenges
  • high-stakes, high-visibility work
  • Action-biased and momentum-driven
  • Equally comfortable in a room with a C-level executive and in the weeds with an engineering team lead
  • proactive problem-solver who surfaces blockers and risks before they become escalations
  • leading projects and programs in highly technical environments
  • extract delivery signal from engineering deep-dives
  • connect technical decisions to program risk
  • push back on sequencing when it matters
  • Disciplined about visibility
  • stakeholders always know where things stand
  • surprises don't happen on your programs
  • trusted partner who earns credibility through follow-through
  • build relationships that make cross-functional coordination feel easy
  • Calm and effective in ambiguity
  • create structure where none exists
  • adapt when priorities shift
  • Comfortable using AI to solve problems and streamline workflows
  • actively looking for new ways to put it to work
  • clear owners and success criteria
  • high-impact, cross-functional programs
  • ensuring dependencies are visible and delivery stays on track
  • executive-ready status reporting
  • consistent confidence in program and portfolio health
  • mitigate risks across programs before they impact delivery timelines or stakeholder trust
  • technical dependencies, and sequencing trade-offs
  • Use AI tools in your own program execution
  • build lightweight workflows, skills, or automations
  • make status reporting, risk tracking, and stakeholder updates faster and more reliable
  • roadmap planning and resource forecasting
  • program and portfolio level
  • retrospectives and continuous improvement practices
  • building a high-performing, AI-native program management function
  • CIO organization
  • complex, cross-functional technical initiatives
  • high-growth SaaS or enterprise tech environment
  • early-stage idea
  • build it into structured delivery with measurable outcomes
  • move fast and drive velocity
  • keeping programs progressing through ambiguity and unknowns
  • managing executive-level stakeholder relationships
  • C-level communication
  • poise and precision
  • working alongside technical team leads in AI, engineering, data, or platform contexts
  • follow the technical conversation
  • ask the right questions
  • push back when sequencing or assumptions need challenging
  • Familiarity with AI tools and an automation instinct
  • naturally ask "how can I use AI to do this?"
  • applied it to improve how you work
  • project tracking and program management tools
  • Exceptional communicator across technical and non-technical audiences
  • written updates, verbal alignment, and executive readouts
  • drive accountability and alignment across teams without formal authority
  • Hands-on experience using agentic AI tools
  • build skills, automations, or multi-step workflows
  • not just to use AI passively
  • Familiarity with AI/ML platform delivery
  • data engineering workflows
  • data warehousing
  • MLOps practices
  • high-growth cybersecurity or SaaS company
  • remote-first, distributed team environment
  • Exposure to cloud infrastructure
  • intersects with data and AI platform programs
  • Agile/Scrum ceremonies and sprint-level delivery
  • program-level ownership