platform engineer Machine Learning

The company provides AI SaaS solutions and is building an internal platform that enables ML teams to train, deploy, observe, and operate production models independently.

• Build and evolve an internal platform for ML training and deployment;
• Operate production ML workloads on Kubernetes;
• Develop model lifecycle workflows using MLflow;
• Automate cloud infrastructure using Terraform;
• Build containerised ML environments with Docker;
• Implement GitOps-based deployment and infrastructure workflows;
• Develop Python tooling and platform automation;
• Create self-service capabilities for ML Engineers and Data Scientists;
• Improve model observability, reproducibility, and operational reliability;
• Work across AWS and/or GCP infrastructure.

• 4+ Years in MLOps, ML Platform Engineering, Platform Engineering, or similar roles;
• Python;
• Kubernetes;
• MLflow;
• Terraform;
• Docker;
• AWS and/or GCP;
• GitOps;
• Strong understanding of production ML workflows;
• Nice to have: Argo CD / Flux, Kubeflow, GPU infrastructure, Ray, KServe or model-serving platforms, Prometheus / Grafana / OpenTelemetry, feature stores, experience building Internal Developer Platforms or self-service infrastructure.

• No conditions specified