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.
You think from first principles. You want to understand the why behind technical choices, cut through the noise to the core problem, and design elegant solutions from the ground up.
You take ultimate ownership. You’re relentless about reliability, efficiency, and user impact for everything you and your team build.
You’re a builder first. You’ve earned the right to set direction because you’ve shipped and operated the systems you’re now designing. You still open the editor.
What you’ll bring
10+ years in software engineering, with at least 4 years focused on building and operating large-scale infrastructure, platform engineering, or distributed systems in production.
Proven experience leading technical projects and mentoring or managing a team of engineers.
Deep, production-level expertise designing, building, and operating systems on Kubernetes, including GPU scheduling.
Expert-level Python, and the distributed systems judgment to reason clearly about failure, backpressure, and cost at scale.
Hands-on experience building and managing cloud infrastructure on AWS and GCP with infrastructure as code (Terraform or Pulumi).
Strong, practical grounding in modern software development practice: Git, CI/CD, monitoring, alerting, and automated testing.
Bachelor’s or Master’s degree in computer science, engineering, or a related technical field, or equivalent practical experience.
Highly desirable
Workflow orchestration frameworks such as Argo Workflows, Kubeflow Pipelines, Flyte, or Ray and KubeRay.
Designing and managing large-scale, GPU-intensive ML workloads, and GPU capacity strategy across more than one cloud.
Deep familiarity with the MLOps and LLMOps ecosystem: model registries, feature stores, serving frameworks, vector databases, and RAG systems.
Advanced Kubernetes concepts, including service mesh (Istio), custom operators, and multi-cluster networking.
Petabyte-scale data pipelines, or geospatial and computer vision workloads.
A track record of contributing to open-source projects in the cloud-native or MLOps space.

Employment type
Full-time

Work arrangement
Hybrid