Principal AI Platform Engineer

As Principal AI Platform Engineer, you’ll be the key architect and technical owner of the platform that powers Nearmap AI innovation. You’ll lead a team of 3 to 4 mid-level and Senior Engineers, set the technical vision for our ML infrastructure, and drive its evolution.

This is a leadership role for a seasoned engineer who thinks in systems. Nearmap captures and processes aerial imagery at a scale that breaks most infrastructure: thousands of EKS nodes running batch inference, distributed GPU training across two clouds, real-time model endpoints behind customer products, and LLM and agentic systems moving from prototype into production. You won’t only build on that platform. You’ll define what it becomes.

Reporting to the Director, AI Systems Engineering, your customers are the AICV product teams: AI Model R&D, Computer Vision, Insurance Data Science, Agentic AI, and AI Map Data. Your objective is a robust, scalable, efficient ecosystem that acts as a force multiplier for the whole AI organisation.

To be clear about the boundary: this is a platform role, not an application role. You build what those teams build on, not the customer-facing products themselves.

Day to day, you’ll:

• Define and own the technical roadmap for core ML infrastructure: workflow orchestration on Kubernetes, distributed training, batch inference at thousands-of-nodes scale, real-time serving on Ray, and LLMOps.

• Make the critical design calls and evaluate new technologies, from orchestrators and serving frameworks to vector databases.

• Lead and mentor a team of 3 to 4 mid-level and senior ML systems engineers, and own technical hiring for the platform team.

• Spearhead the highest-risk work yourself: multi-cloud GPU capacity strategy, foundational platform components, and the observability stack.

• Write production Python for the hardest parts of the shared platform, and prototype new capabilities before the team commits to them.

• Establish and champion MLOps and AIOps best practice across the organisation, covering automation, infrastructure as code, CI/CD, and security through the AI lifecycle.

• Partner with Data Science and ML Engineering teams, turning their challenges into an actionable platform roadmap.

• Own service level objectives and GPU cost efficiency across AWS and GCP, lead major incident response, and build detection for the ways production ML fails quietly.

• You’re a strategic pragmatist. You understand complex systems deeply and make practical trade-offs. You build for the future without over-engineering for the present.

• You’re a collaborative leader. You elevate your team by sharing knowledge, setting high standards, and fostering an environment where the best ideas win.