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

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

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