What you’ll be doing

• Own delivery of AI workloads and AI-enabled products end-to-end: from discovery through build, launch, and iteration
• Convert loosely defined ideas into delivery plans, milestones, and measurable outcomes
• Lead engineering execution across multiple teams, ensuring clear ownership boundaries, interfaces, and ways of working
• Drive pragmatic technical direction across AI systems: LLM/ML deployment patterns, model lifecycle, monitoring, and iteration
• Ensure production readiness: security, reliability, observability, governance, and cost awareness (FinOps mindset)
• Unblock teams through active problem-solving, dependency management, and escalation when needed
• Balance trade-offs across speed, quality, risk, and cost and communicate them clearly
• Manage stakeholder expectations with calm, credible leadership; run steering conversations and provide transparent updates
• Build a culture of continuous improvement and innovation, with disciplined delivery under strict timelines

Experience and capabilities (essential)

• Proven experience leading engineering delivery for data-heavy, AI-enabled, or platform products
• Strong understanding of modern AI concepts: ML systems, LLMs, data dependencies, evaluation, and operational risks
• Track record managing complex deliveries across multiple teams and stakeholders
• Comfortable operating in federated/domain-oriented environments with shared ownership
• Excellent communication: able to align senior stakeholders and guide teams through ambiguity
• Solid grasp of production engineering fundamentals: cloud, reliability, security, monitoring, CI/CD

Desirable

• Experience delivering AI in regulated or high-stakes environments (e.g., financial services)
• Familiarity with AI governance, ethics, and emerging regulation (e.g., EU AI Act)
• Exposure to LLM platforms (e.g., Bedrock / Foundry) and multi-tenant cost controls
• Consulting/client-facing delivery leadership and workshop facilitation