This is not a research role, and it is not a generic support role. It is an implementation-heavy, building-focused engineering role on a small team responsible for making in-house AI capabilities easier to package, evaluate, deploy, promote, operate, and improve. AI Platform Engineering exists to shorten the path from emerging AI capability to reliable production impact.

We build the shared systems, standards, and delivery pathways that let in-house models and AI capability packages move from candidate state into observable, rollback-safe production operation. Our work sits at the junction between model development, runtime systems, evaluation, and delivery. We enable the broader AI Platform division by making it faster and safer to ship new capabilities, improve existing ones, and learn from production behavior.

As a founding team member, you will help design, build, and improve the systems that connect AI capability development to production reality. Depending on your strengths, that may include work such as:

• Improving how model and capability artifacts are packaged, versioned, promoted, and rolled back.
• Building or improving deployment and release pathways for AI-backed services.
• Enabling shadow-serving, staged rollout, and candidate-versus-incumbent comparison.
• Strengthening runtime behavior, observability, and debugging for model-backed systems.
• Building or automating evaluation systems that make release decisions evidence-based.
• Reducing bespoke coordination and strengthening the shared rails used by multiple AI teams.

The exact balance will depend on your background and the team’s evolving needs. What will not vary is the mission: your work should make the broader AI Platform organization faster, safer, and more effective at turning in-house AI capability into production reality.

To succeed in this role, you will need to bring:

• A Bachelor's degree in Computer Science, Engineering, or equivalent related experience.
• 2 to 6 years of professional software engineering experience, with a proven track record of shipping production infrastructure or real systems that matter.
• Experience in writing solid, maintainable production code and applying strong software engineering fundamentals to solve complex debugging challenges.
• Experience in operating within ambiguous, cross-functional environments where requirements evolve and interfaces are real.
• Expertise in building for reproducibility, operability, and rollout safety, focusing on the quality of change rather than just local implementation.

Experience with cloud infrastructure, containerized environments, managed ML platforms, or service orchestration systems is a plus.

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