Senior Staff AI Platform Engineer

Our Purpose At SentinelOne, we are driven by a clear purpose: to give the advantage to those who secure our future. As AI reshapes how organizations build, operate, and innovate, the responsibility to protect them becomes more critical than ever. When you join SentinelOne, your work helps protect global enterprises, critical infrastructure, and the technologies shaping tomorrow. If you are motivated by meaningful challenges and want your impact to be real, measurable, and global, you will find purpose here. About Us SentinelOne is a company at the intersection of AI and security, pioneering a new operating model for cybersecurity. Our AI-native platform unifies protection across endpoint, cloud, identity, data, and AI systems to deliver autonomous detection and response with clarity and speed. By combining real-time analytics, intelligent automation, and a unified data foundation, we reduce noise, simplify complexity, and empower security teams to focus on what truly matters. Our teams are builders, problem-solvers, and innovators committed to shaping the future of security. If you are excited to solve hard problems alongside talented, mission-driven people, we invite you to help us build a safer future for humanity. What Are We Looking For? 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. As a Senior Staff AI Platform Engineer, you will be tasked with serving as a hands-on builder of SentinelOne's enterprise AI platform, including the Gateway, Harness, and Semantic Layers that every internal AI use case runs on top of. You will write the production code that governs, observes, secures, and scales AI activity across the company, working closely with the Sr. Director of Enterprise AI Platform Engineering and the rest of the platform team to take the architecture from design into running infrastructure. What Will You Do? Primary responsibilities include: Design, build, and operate components of the AI Gateway including centralized identity and authentication/authorization, token budgeting, DLP and content guardrails, multi-model routing and failover, MCP allow-listing, and request-level audit logging. Build and maintain pieces of the Harness layer including agent orchestration (LangGraph or equivalent), prompt construction and context management, memory and state handling across multi-turn interactions, and model abstraction across providers such as AWS Bedrock and Google Vertex. Develop and extend production services in Python (FastAPI) and pydantic.ai for LLM-powered components, and contribute to the React/TypeScript surfaces that expose platform capabilities to internal teams. Implement MCP/tool wiring for internal and SaaS-embedded agents, ensuring every caller regardless of origin passes through the same policy controls. Contribute to the Claude and Gemini Enterprise plugin framework by building, testing, and hardening role-based skills and agents, and help mature the pipeline for how plugins are authored, evaluated, and deployed. Instrument the platform for observability including metrics, tracing, and audit trails, and participate in on-call and operational support for platform services. Write clear technical documentation and participate in architecture and code reviews, holding a high bar for quality, security, and maintainability. Partner with engineers across Enterprise Data, Enterprise Apps, Product Development, and Infosec to integrate the platform with governed data sources and shared architectural contracts. Work with the Sr. Director and model evaluation tooling to help close the loop between evaluation results and model selection, prompt tuning, and routing decisions. What Skills and Knowledge Will You Bring? Ideal candidates will have: 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. 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. 8 or more years of professional software engineering experience; with experience building or operating AI and ML infrastructure in a production environment. Strong Python skills, ideally with FastAPI, and comfort picking up frameworks like pydantic.ai for LLM-powered components; work