Technical Project Manager

• Lead client data-engineering, artificial-intelligence-engineering and strategy engagements from contract signature to client acceptance and production handover
• Deliver data and artificial-intelligence engagements on time, on budget and to agreed scope
• Convert proposals and statements of work into release plans, prioritised backlogs, milestones, acceptance criteria, responsibility matrices and governance cadences
• Facilitate Scrum events and manage Kanban workflow, work-in-progress limits and flow measures
• Adapt governance for fixed-fee milestones, formal stage gates and contractual or regulatory requirements
• Lead teams of typically three to ten engineers, remove blockers, agree capacity and commitments, and coach junior team members
• Act as the client’s day-to-day point of contact; chair steering committees, prepare status reports and manage expectations
• Track budgets, forecast cost at completion, protect engagement margins and manage scope changes and commercial approvals
• Maintain risks, assumptions, issues and dependencies logs and ensure testing, evaluation thresholds, documentation and control evidence
• Steer AI delivery through stop, pivot and scale criteria, evaluation-based acceptance, model-risk and data-privacy sign-offs, and human review design
• Coordinate release readiness, including security and privacy reviews, monitoring, rollback and support ownership
• Plan GxP validation and evidence for regulated healthcare and life-sciences engagements
• Plan cut-over, hypercare and transition to the client or CFGI managed service with runbooks, service levels and support arrangements
• Build reusable delivery plans, templates and estimation baselines and support proposals with estimates, staffing plans and delivery approaches

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