Senior Technical Project Manager – AI Engineering

About the role

Delivery and technical leadership

• Translate business objectives into AI roadmaps, milestones, measurable outcomes, and ROI targets; establish accountability, acceptance criteria, and delivery baselines.
• Manage dependencies and capacity, remove engineering bottlenecks, coordinate cross-product work, and mitigate delivery risks.
• Assess model architectures, algorithms, infrastructure, and system designs against business and technical requirements.
• Guide trade-offs across model accuracy, inference latency, infrastructure cost, operational complexity, scalability, and resilience.
• Promote reusable AI components, evaluation frameworks, infrastructure, and architecture patterns across products.

Quality, governance, and communication

• Build evaluation pipelines, representative benchmarks, statistical quality metrics, and measurable success criteria across the AI lifecycle.
• Distinguish proofs of concept and validated prototypes from production-ready systems; require measurable results and reproducible evaluations before release.
• Ensure plans include fail-safes, real-time monitoring, human-in-the-loop escalation, security controls, and operational fallback strategies.
• Provide executive visibility into progress, dependencies, resource needs, delivery confidence, technical decisions, and risks.
• Define readiness, completion, quality-gate, and deployment standards; lead focused planning, risk reviews, demonstrations, and process improvements.

Required qualifications and experience

• Extensive experience as a Senior Technical Project Manager, Technical Program Manager, or Engineering Leader delivering complex, high-value, multimillion-dollar software initiatives.
• Hands-on experience building, fine-tuning, evaluating, and deploying production AI/ML models; basic prompting or third-party API integration alone is insufficient.
• Strong technical leadership in feasibility assessment, architectural trade-offs, and delivery of complex initiatives.
• Deep knowledge of AI workflows, model evaluation, statistical metrics, deployment infrastructure, latency, cost optimization, and production monitoring.
• Experience managing complex roadmaps, multidisciplinary engineering teams, technical risks, and delivery accountability.
• Exceptional communication with researchers, engineers, product leaders, and C-suite executives.
• Strong commercial and strategic understanding of engineering investments and business value.

Candidate profile and success

• The role requires a technical operator who combines disciplined delivery with genuine hands-on AI engineering expertise.
• Success means predictable AI product delivery, evidence-based engineering decisions, rigorous production releases, early risk resolution, executive visibility, and efficient engineering teams.