Head of Artificial Intelligence

At The ReWork Group, we partner with high-growth startups and forward-thinking companies to build the future.

As the

Head of AI

, you'll own their AI-enabled, eCommerce-focused agenda. AI isn't a line on their roadmap. It's the through-line of how they compete. We're looking for the person who will own how AI shows up everywhere it matters: the product they sell, the way they operate, and how their engineers build.

This is the rare seat where the role is genuinely broad. You're building AI-powered experiences that help brands manage, understand, and grow their eCommerce operations. Internally, AI is already changing how their engineers work, and their only at the beginning of what that unlock

This role is ideal for a builder, someone leading teams of machine learning engineers and data scientists to build, train, and deploy AI models into production.

What You'll Do:

• Own the roadmap and execution for all AI-native product capabilities.
• Ensure AI is embedded across workflows and engineering practices.
• Drive the transition to agentic development.
• Own the AI architecture decisions that matter: model selection, agentic pipeline design, LLM integration patterns, inference infrastructure, and how AI interacts with the data layer
• Lead the data team's platform investments unlock what AI needs, and that product and engineering are building toward a coherent AI strategy rather than separate experiments.

You May Be a Good Fit:

• 8\+ years in data and ML roles in SaaS, including 3\+ years leading a function spanning data engineering, ML/data science, and analytics
• Demonstrated experience taking ML or AI models from research into production at enterprise scale, with model governance, validation, and explainability practices in place.
• Practical experience building applied AI solutions on top of proprietary datasets using foundation models, covering the full lifecycle: retrieval architectures, model adaptation, embedding infrastructure, and evaluation frameworks.
• Clear, defensible point of view on what makes a retrieval-based AI system production-ready versus a proof of concept, covering evaluation pipelines, observability, retrieval quality measurement, and failure modes. You've had to defend that view to a customer, compliance team, or auditor.
• Experience building AI model governance frameworks in a regulated or enterprise context: auditability, drift detection, regression testing, explainability, and rollback protocols.