Senior Technical Project Manager – AI Engineering

Department: AI Engineering / Product Delivery

Position: Senior Technical Project Manager (AI Engineering)

About the Role

We are seeking an exceptional Senior Technical Project Manager with proven, hands-on AI engineering experience to lead and accelerate our AI product roadmap from concept through production.

This is a high-impact leadership role that bridges deep technical execution, strategic business objectives, and C-suite decision-making. You will be responsible for transforming cutting-edge AI capabilities into scalable, reliable, production-grade enterprise solutions.

Unlike traditional project managers who primarily track tasks and timelines, you will bring genuine engineering expertise, strong technical judgment, and exceptional delivery discipline.

You will serve as a critical authority on delivery quality, execution speed, technical rigor, and production readiness, driving everything from project scoping and architectural trade-offs to statistical evaluation and successful deployment.

Key Responsibilities

• High-Velocity Delivery & Strategic Execution

End-to-End Ownership: Translate complex business objectives into actionable AI project roadmaps, clearly defined milestones, measurable outcomes, and quantifiable ROI targets.

Execution Excellence: Establish clear accountability, rigorous acceptance criteria, realistic delivery baselines, and measurable performance standards to ensure consistent, on-time execution.

Dependency & Capacity Management: Proactively eliminate engineering bottlenecks, coordinate cross-product dependencies, and optimize resource allocation across multidisciplinary teams.

Proactive Risk Management: Identify technical, operational, and delivery risks early, drive rapid resolution, and present actionable mitigation strategies to executive leadership.

Scope & Change Governance: Maintain disciplined change-control processes, ensuring scope adjustments are justified, documented, and aligned with business priorities and revised delivery commitments.

• Technical Authority & Engineering Alignment

Architecture & Feasibility Assessment: Collaborate closely with AI engineering leads to evaluate model architectures, algorithms, infrastructure, and system designs against business and technical requirements.

Pragmatic Engineering Leadership: Challenge unnecessary technical complexity, validate engineering estimates, and advocate for simpler, more reliable solutions when sophisticated AI approaches are not justified.

Strategic Trade-Off Analysis: Guide technical decisions by evaluating model accuracy, inference latency, infrastructure cost, operational complexity, scalability, and system resilience.

Cross-Product Standardization: Identify opportunities to reuse AI components, evaluation frameworks, engineering infrastructure, and architectural patterns across multiple products and teams.

• Production Quality, Guardrails & Gatekeeping

Rigorous Evaluation Frameworks: Establish robust AI evaluation pipelines, representative benchmarks, statistical quality metrics, and measurable success criteria throughout the AI development lifecycle.

Production Readiness: Enforce clear distinctions between proof-of-concept demonstrations, validated prototypes, and genuinely production-ready systems.

Evidence-Based Quality Gates: Require measurable performance results, reproducible evaluations, and documented acceptance criteria before approving production releases.

Enterprise AI Guardrails: Ensure appropriate fail-safes, real-time monitoring, human-in-the-loop escalation mechanisms, security controls, and operational fallback strategies are incorporated into delivery plans.

• Executive Visibility & Strategic Alignment

Evidence-Based Progress Tracking: Validate project progress through tangible engineering evidence, including deployment logs, evaluation results, working demonstrations, and production performance metrics.

C-Suite Communication: Act as the single source of truth for executive stakeholders, translating complex technical challenges into clear business risks, strategic options, and actionable decisions.

ROI-Driven Prioritization: Continuously challenge scope creep, duplicated engineering efforts, and low-value initiatives to maximize business impact and engineering efficiency.

Strategic Accountability: Maintain transparent reporting on milestones, dependencies, delivery confidence, resource requirements, and key technical decisions.

• Process Optimization & Delivery Standardization

Operational Excellence: Define and enforce robust Definitions of Ready (DoR), Definitions of Done (DoD), quality gates, and production deployment standards.

Efficient Delivery Practices: Lead focused planning sessions, technical risk reviews, milestone demonstrations, and decision-making forums that accelerate delivery without unnecessary administrative overhead.

Continuous Improvement: Identify recurring delivery inefficiencies and implement scalable processes that improve engineering productivity, predictability, and product quality.

Required Qualifications & Experience

Proven Delivery Track Record: Extensive experience as a Senior Technical Project Manager, Technical Program Manager, or Engineering Leader delivering complex, high-value, multimillion-dollar software initiatives.

Hands-On AI Engineering Experience: Demonstrable experience building, fine-tuning, evaluating, and deploying production AI/ML models, extending well beyond basic prompting or third-party API integrations.

Strong Technical Leadership: Proven ability to challenge engineering assumptions, assess technical feasibility, navigate architectural trade-offs, and drive complex initiatives to successful delivery.

End-to-End AI Lifecycle Expertise: Deep understanding of AI development workflows, model evaluation, statistical performance metrics, deployment infrastructure, inference latency, cost optimization, and production monitoring.

Advanced Delivery Management: Expertise in managing complex roadmaps, coordinating multidisciplinary engineering teams, mitigating technical risks, and maintaining delivery accountability.

Executive-Level Communication: Exceptional ability to communicate confidently with AI researchers, software engineers, product leaders, and C-suite executives.

Commercial & Strategic Acumen: Strong understanding of how engineering investments translate into measurable business value, operational efficiency, and sustainable competitive advantage.

Ideal Candidate Profile

Who You Are

• You are a high-caliber technical operator who combines disciplined project execution with genuine, hands-on AI engineering expertise.
• You understand how AI systems are designed, trained, evaluated, deployed, and maintained—and you know what it takes to deliver them reliably at enterprise scale.
• You are comfortable challenging technical assumptions, demanding empirical evidence, making difficult prioritization decisions, and holding teams accountable for measurable outcomes.
• You bring the technical credibility to engage deeply with engineering teams and the strategic judgment to influence executive leadership.
• Above all, you are focused on turning ambitious AI initiatives into measurable, production-ready business outcomes.

Who You Are Not

• A traditional, non-technical project manager who relies solely on Jira tickets, status reports, and delivery dashboards.
• A coordinator who accepts engineering estimates and technical decisions without understanding or challenging their underlying assumptions.
• A pure AI researcher focused exclusively on experimentation without accountability for production deployment, business impact, or delivery timelines.
• A process-heavy manager who prioritizes ceremonies and documentation over tangible engineering progress.

What Success Looks Like

Success in this role means

• AI products are delivered predictably, with clear ownership, realistic milestones, and measurable business outcomes.
• Engineering decisions are evidence-driven, with transparent trade-offs between quality, performance, cost, and delivery speed.
• Production releases meet rigorous standards for reliability, security, evaluation quality, and operational readiness.
• Technical risks are identified and resolved early, rather than escalating into missed deadlines or production failures.
• Executive leadership has complete visibility into delivery progress, investment priorities, and strategic risks.
• Engineering teams move faster and more effectively, supported by clear priorities, reusable systems, and minimal unnecessary bureaucracy.
• The ultimate objective: Deliver high-quality, production-grade AI systems at speed, with technical excellence, commercial accountability, and measurable enterprise impact.