Platform Engineer to AI: Building ML Platforms

Transition from platform engineering to ML platform roles by applying your infrastructure expertise to AI systems. As a platform engineer, you already understand the critical foundations, Kubernetes orchestration, infrastructure as code, CI/CD pipelines, and developer experience optimization. ML platforms need these exact skills, but applied to a new domain: model training infrastructure, feature stores, model serving systems, and experiment tracking. Your experience building internal developer platforms translates directly to building internal ML platforms that data scientists and ML engineers depend on daily. The gap isn't about learning entirely new concepts. It's about understanding ML-specific patterns like GPU scheduling, model versioning, feature engineering pipelines, and the unique observability challenges of ML systems. You'll learn to build self-service ML infrastructure that abstracts away complexity while maintaining the reliability and scalability standards you already enforce. Organizations desperately need engineers who can bridge the gap between traditional DevOps and the specialized needs of ML workloads. Your platform mindset, thinking in terms of golden paths, developer productivity, and infrastructure abstraction, is exactly what ML teams lack. Timeline: 4-6 months to become a capable ML platform engineer, with continuous learning as the field evolves rapidly.