Job Title: AI Project Manager
Location: San Diego, CA
Contract length: 3 months
Schedule: Fulltime, 40 hrs/week
Pay: $103/hr to $113/hr, DOE

Job Overview:
We are scaling AI from pilots to production across R&D, Commercial, Manufacturing, and Corporate functions. This six-month contract engagement runs delivery for an AI portfolio already in motion -- enterprise search, document Q&A, report drafting, meeting intelligence, request triage, and emerging agentic automation -- delivered by small pods against a prioritized roadmap. This is a hands-on delivery role rather than a status-reporting role. The contractor is accountable for capabilities reaching production and achieving measurable adoption within the engagement window.

Key Responsibilities:

• Portfolio & Delivery
  • Portfolio execution. Sequence an active AI roadmap against finite pod capacity. Make and document the tradeoff decisions on what proceeds, what defers, and what is stopped.
  • Project delivery. Drive initiatives from intake through pilot to production: scope, milestones, dependencies, risks, and launch. Resolve blockers for delivery pods.
  • Agentic AI delivery. Lead delivery of agentic and multi-step automation capabilities: scoping the workflows suited to autonomous execution, defining human-in-the-loop checkpoints and escalation paths, and coordinating the guardrails, monitoring, and rollback provisions required under the agentic AI policy.
• Governance & Partners
  • Governance interface. Prepare and steward submissions through the AI Policy Review Committee. Engage Legal, Security, IT, and Compliance early in the delivery cycle so that required approvals are sequenced into the plan rather than encountered as late-stage obstacles.
  • Vendor and platform coordination. Manage implementation partners and SaaS vendors against SOWs and timelines. Hold partners accountable to committed dates.
• Adoption & Communication
  • Adoption. Define and execute the adoption plan for each capability: pilot cohort design, utilization tracking, and enablement delivered alongside the AI literacy program, with adoption reported against defined targets.
  • Executive communication. Produce the status, decision, and steering materials that go to VPs and the executive team.

• 5+ years managing technical delivery, including demonstrated experience leading AI or machine learning projects from concept through production at enterprise scale.
• Direct delivery experience with generative AI initiatives such as enterprise search, document intelligence, or conversational assistants.
• Practical experience delivering agentic AI: workflows involving tool use, multi-step task execution, orchestration across systems, and the design of human-in-the-loop controls, guardrails, and monitoring appropriate to autonomous execution.
• Working fluency across enterprise AI and automation platforms, including Claude, Microsoft 365 Copilot, Glean, Power Automate, and UiPath, or comparable equivalents. Sufficient depth to scope realistically, evaluate vendor claims, and translate between technical teams and business stakeholders.
• Supporting experience in data and analytics delivery, with an understanding of how data readiness and platform architecture constrain AI outcomes.
• Demonstrated success delivering in a regulated environment where Legal, Security, and Compliance approvals sit on the critical path.
• Comfort in the Microsoft ecosystem: Azure, Fabric, Entra ID, SharePoint, and Teams.
• Strong executive-level written and verbal communication, with the ability to produce concise materials for senior leadership.
• Proven ability to onboard rapidly and operate independently within a small team.

• Biopharma, life sciences, or another regulated industry, with familiarity handling GxP or MNPI considerations.
• Experience supporting an AI governance or review committee process.
• Exposure to data governance: classification schemes, data owner and steward models, and access frameworks.
• Vendor management and SOW or contract administration experience.
• PMP, Agile, or equivalent certification.

• Prioritized initiatives delivered to their committed milestones, with any scope changes documented and formally agreed.
• A maintained, transparent prioritization model that ties delivery sequence to pod capacity and business value, with reprioritization decisions recorded and communicated.
• Prioritized capabilities transitioned from pilot to production with measured and reported adoption, rather than deployment counts.
• Governance submissions for prioritized work cleared without avoidable rework or schedule slip.
• Delivery cadence, intake process, and documentation established such that the internal team can sustain the portfolio at engagement close.