AI Platform Engineer Jobs:
Build the Systems ML Teams Depend On

MLOps Engineers focus on the ML lifecycle: training, deploying, and monitoring specific models. AI Platform Engineers build the platforms and internal tools that MLOps Engineers use. Think of it this way: MLOps deploys a model to production. Platform Engineering builds the system that makes that deployment possible. Platform roles are more focused on developer experience, self-service tooling, and organizational scale. MLOps is closer to the models themselves.

The core stack includes: (1) Kubernetes and container orchestration at scale, (2) Internal developer platform tools (Backstage, custom CLIs, self-service portals), (3) ML infrastructure components (Ray, KubeFlow, MLflow, feature stores like Feast or Tecton), (4) Infrastructure as Code (Terraform, Pulumi), (5) Observability and monitoring for ML-specific metrics. The differentiator is building for internal users, not external customers. Developer experience design matters as much as raw infrastructure skills.

AI Platform Engineer salaries typically range from $150K-$180K for mid-level roles to $180K-$220K+ for senior positions. Staff-level platform engineers at major tech companies can exceed $300K total compensation. These roles command premiums because they require rare combinations of platform engineering, ML systems knowledge, and developer experience expertise. Companies building serious ML capabilities often pay above market to attract this talent.

Look for companies with significant ML teams (50+ data scientists/ML engineers) who need dedicated platform support. Top employers include: (1) Tech giants: Netflix, Spotify, Uber, LinkedIn, Meta, Google, (2) AI-native companies: OpenAI, Anthropic, Scale AI, Databricks, (3) Fintech/trading: Two Sigma, Citadel, Stripe, (4) Enterprise tech: Salesforce, Adobe, Snowflake. Smaller companies often combine this role with MLOps. Look for job titles like 'ML Platform Engineer', 'AI Infrastructure Engineer', or 'Machine Learning Platform' in listings.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.