Senior Staff AI Platform Engineer

Join SentinelOne as a Senior Staff AI Platform Engineer, where you will be a key player in building and operating the enterprise AI platform. You will work closely with the Sr. Director of Enterprise AI Platform Engineering and the platform team to design and implement various components of the AI Gateway and Harness layer. You will also contribute to the Claude and Gemini Enterprise plugin framework, instrument the platform for observability, and collaborate with engineers across different departments. This role offers a comprehensive benefits package, including unlimited time off, flexible spending accounts, and a 401K plan.

• Concevoir, construire et exploiter des composants de la passerelle AI, y compris l'identité centralisée et l'authentification/autorisation.
• Développer et étendre des services de production en Python (FastAPI) et <pydantic.ai> pour les composants alimentés par LLM.
• Instrumenter la plateforme pour l'observabilité, y compris les métriques, le traçage et les pistes d'audit.
• We’re looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts
• Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes
• 8 or more years of professional software engineering experience; with experience building or operating AI and ML infrastructure in a production environment
• Exposure to or curiosity about the Model Context Protocol (MCP) and the challenges of governing tool access for agents operating across enterprise systems
• Familiarity with enterprise AI deployments such as Claude Enterprise (Anthropic) or Gemini Enterprise is a plus, including how they are administered and how access and policy controls work
• Comfort operating in a fast-moving, still-forming platform environment where you are energized by ambiguity and enjoy turning a rough architecture into working, reliable infrastructure
• Practical experience with agent orchestration frameworks (LangGraph or equivalent) and calling hosted model providers such as AWS Bedrock or Google Vertex, with a working understanding of how context windows, memory, and tool-calling actually behave in production, not just conceptually
• Solid software engineering fundamentals including clean, tested, production-grade code, strong API design skills, and the ability to give and receive feedback well in code and architecture reviews
• Hands-on experience building production services that sit in front of multiple consumers such as an API gateway, internal platform, or data platform, with real exposure to authentication/authorization, rate limiting, observability, or audit logging; direct AI and LLM platform experience is a strong plus but not required
• Strong Python skills, ideally with FastAPI, and comfort picking up frameworks like <pydantic.ai> for LLM-powered components; working familiarity with React and TypeScript is a plus