Senior Technical Project Manager – AI Engineering

Role Summary

Key Responsibilities

1. Project Planning & Delivery Management

• Scope & Schedule Ownership: Translate business objectives into clear project scopes, milestones, delivery schedules, and quantifiable success metrics.
• Execution & Accountability: Define task ownership, acceptance criteria, effort estimates, and delivery commitments; track day-to-day progress against agreed baselines.
• Dependency & Capacity Management: Coordinate cross-product dependencies, resolve resource bottlenecks, and manage competing priorities across engineering teams.
• Risk & Blocker Mitigation: Proactively identify delivery blockers, lead rapid resolution, and escalate critical issues to leadership with concrete recommendations.
• Change Governance: Manage changes to scope, schedule, or resources with decision-makers while establishing revised baselines.

2. AI Technical Oversight & Engineering Alignment

• Technical Feasibility Assessment: Partner with engineering leads to evaluate whether proposed AI/ML models and architectures suit the business problem.
• Rigor & Simplicity: Challenge unrealistic engineering estimates, overly complex architectures, and premature technical assumptions. Advise teams when simple heuristics or standard software solutions are preferable to AI.
• Trade-Off Analysis: Evaluate technical trade-offs between model accuracy, response latency, operational cost, maintainability, and system reliability.
• Cross-Product Reuse: Identify opportunities to reuse AI components, evaluation frameworks, and architectural patterns across products.

3. Quality Assurance, Evaluation & Release Criteria

• Evaluation Frameworks: Ensure every AI project milestone incorporates rigorous evaluation methods, representative benchmarks, and statistical quality targets.
• Demonstration vs. Production Gatekeeping: Distinguish early proof-of-concept demos from true production readiness; challenge unsupported claims regarding model accuracy or completion.
• Production Guardrails: Ensure project plans account for real-time monitoring, failure-handling/fallback mechanisms, data security, and human-in-the-loop oversight.

4. Visibility, Reporting & Priority Alignment

• Evidence-Based Reporting: Verify progress using tangible evidence (demonstrations, test results, evaluation reports, and deployment logs) rather than informal updates.
• Executive Transparency: Maintain a single source of truth for product status, key risks, open decisions, and next steps for executive leadership.
• Scope Control: Identify scope creep, redundant efforts, or technical work lacking clear business ROI, making the impact of proposed changes explicit.
• Decision Tracking: Document key technical and operational decisions, ensuring all delivery plans reflect agreed outcomes.

5. Delivery Process Optimization

• Process Standardisation: Establish clear, practical Definitions of Ready (DoR), Definitions of Done (DoD), and production release standards.
• Focused Ceremonies: Facilitate efficient planning sessions, technical risk reviews, and milestone demos that enhance predictability without adding administrative overhead.

Required Qualifications & Experience

• Experience: Proven track record as a Technical Project Manager, Technical Program Manager, or Engineering Lead delivering complex software projects.
• Hands-On AI Background: Direct, practical experience building, testing, or deploying AI/ML systems in production environments (beyond basic LLM prompting or API wrapper usage).
• Delivery Track Record: Demonstrated ability to manage multi-faceted project plans, balance cross-functional trade-offs, and ship products on schedule.
• Technical Depth: Deep understanding of AI development lifecycles, evaluation frameworks, model latency/cost trade-offs, and production monitoring requirements.
• Stakeholder Management: Exceptional communication skills—equally adept at discussing evaluation metrics with AI engineers and presenting risk assessments to C-suite executives.
• Analytical Mindset: Data-driven approach to progress tracking, verification, and problem-solving.

Ideal Candidate Profile

• Who this role IS for: A seasoned technical leader who combines rigorous project management discipline with real AI engineering context, demanding evidence of progress and holding teams accountable for production quality.
• Who this role IS NOT for: A non-technical project manager relying solely on ticket tracking, or an AI researcher/specialist uninterested in project execution, schedules, and delivery governance.