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Nearmap is seeking a Principal AI Platform Engineer to architect and own the ML infrastructure platform that powers the company's AI innovation. You will lead a team of 3–4 mid-level and senior engineers, set the technical vision for ML infrastructure, and drive its evolution across a massive scale: thousands of EKS nodes running batch inference, distributed GPU training across AWS and GCP, real-time model endpoints, and LLM/agentic systems moving into production.
This is a platform engineering role, not an application role. Your customers are the AICV product teams (AI Model R&D, Computer Vision, Insurance Data Science, Agentic AI, AI Map Data), and you will build the robust, scalable, efficient ecosystem they depend on.
Day-to-day responsibilities include:
- 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 critical design calls and evaluate new technologies (orchestrators, serving frameworks, vector databases).
- Lead and mentor a team of 3–4 mid-level and senior ML systems engineers; own technical hiring for the platform team.
- Spearhead the highest-risk work: 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 team commitment.
- Establish and champion MLOps and AIOps best practices across the organization (automation, infrastructure as code, CI/CD, security through the AI lifecycle).
- Partner with Data Science and ML Engineering teams to turn their challenges into actionable platform roadmap.
- Own service level objectives and GPU cost efficiency across AWS and GCP; lead major incident response and build detection for silent production ML failures.
You will report to the Director, AI Systems Engineering.
REQUIREMENTS:
- 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 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 (Argo Workflows, Kubeflow Pipelines, Flyte, Ray, KubeRay).
- Designing and managing large-scale, GPU-intensive ML workloads and GPU capacity strategy across multiple clouds.
- Deep familiarity with MLOps and LLMOps ecosystem: model registries, feature stores, serving frameworks, vector databases, RAG systems.
- Advanced Kubernetes concepts: service mesh (Istio), custom operators, multi-cluster networking.
- Petabyte-scale data pipelines or geospatial and computer vision workloads.
- Track record of contributing to open-source projects in cloud-native or MLOps space.
