- Salary
- $6k – $7k/mo
- Location
- Dubai, Dubai, AE
- Type
- Full-time
- Department
- Engineering
- Seniority
- Senior
- Source
- BetterTeam
Description
Senior Technical Project Manager – AI Engineering
Position: Senior Technical Project Manager (AI Engineering)
Department: AI Engineering / Product Delivery
Role Summary:
We are seeking a Senior Technical Project Manager with hands-on AI engineering experience to own AI projects end-to-end—from initial planning and scoping through execution, evaluation, and production release.
Your core responsibility is hands-on project management: owning execution plans, tracking milestones, managing dependencies, and ensuring reliable delivery. Unlike traditional project management roles, this position requires practical experience building and shipping AI systems. You will bridge the gap between engineering and executive leadership, bringing informed technical judgment to scope decisions, architectural trade-offs, and quality gates.
Key Responsibilities:
- 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.
- 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.
- 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.
- 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.
- 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.
Skills
Project Management