Note: The job is a remote job and is open to candidates in USA. SentinelOne is a company at the intersection of AI and security, pioneering a new operating model for cybersecurity. As Sr. Director of AI Enterprise Platform Engineering, you will build the AI foundation that supports all internal AI use cases, overseeing the development and governance of the AI platform that integrates various AI applications and services.
Responsibilities
- Own and execute the roadmap for SentinelOne's enterprise AI platform — the Gateway, Harness, and Semantic Layers — moving us from direct-to-LLM connections to a governed, multi-model control plane
- Design and build the AI Gateway layer: centralized identity and AuthN/Z, token budgeting, DLP and content guardrails, multi-model routing and failover, MCP allow-listing, and a full audit trail across every AI request
- Build and operate the Harness layer: agent orchestration (LangGraph or equivalent), prompt construction and context management, memory and state across multi-turn interactions, model abstraction across Anthropic, OpenAI, Google, and others via providers such as AWS Bedrock and Vertex, and MCP/tool wiring for internal and SaaS-embedded agents
- Own the technical roadmap for SentinelOne's Claude & Gemini Enterprise plugin framework — a growing library of role-based skills and agents that surface AI capabilities to employees in the context of their specific roles. This includes expanding coverage across more roles and use cases, maturing the way plugins are securely authored, tested, and deployed, and continuously improving the quality and reliability of each plugin's outputs
- Partner closely with Enterprise Data, Enterprise Apps, Product Development, and Infosec to connect the platform to governed data sources, align on security controls, and ensure every integration is built on shared architectural contracts
- Govern how agents — whether user-instantiated in Anthropic, Google, or OpenAI products, engineered by SentinelOne teams, embedded in SaaS platforms, or arriving from external partners — interact with our systems, ensuring every caller passes through the same policy controls regardless of origin
- Hire, mentor, and technically lead a team of engineers; set standards, review architecture decisions, and create an environment where strong engineers do their best work
- Communicate platform status, architecture decisions, and risk posture clearly to executive stakeholders
Skills
- You have built at least one production platform — AI or otherwise — that enforced centralized policy across multiple consumers: auth/AuthN/Z, rate limiting, observability, and audit logging. Bonus if that platform touched AI specifically (LLM gateway, model routing, or orchestration), but strong candidates from API platform, data platform, or developer platform backgrounds are equally welcome. What matters is that you can describe what you built, the tradeoffs you made, and what you'd do differently
- Hands-on depth in agent orchestration (LangGraph or equivalent), including experience routing to hosted model providers such as AWS Bedrock and Google Vertex. You understand how context windows, memory, and tool-calling actually work — not just conceptually
- Demonstrated experience designing and implementing an AI gateway — centralized auth, rate limiting, DLP, observability, and audit logging across multiple model providers. Hands-on experience with Kong AI Gateway specifically is a plus
- You have experience building or owning a model evaluation framework — not just running benchmarks, but closing the loop: using eval results to drive model selection, prompt tuning, and routing decisions
- Familiarity with the Model Context Protocol (MCP) and practical experience governing tool access for agents operating across enterprise systems in a secure way
- Hands-on familiarity with enterprise AI deployments — specifically Claude Enterprise (Anthropic) and Gemini Enterprise — including how they are administered, how access and policy controls work, and how they connect to a broader governed AI architecture
- Strong software engineering background — you can write and review production code, understand API design, and hold a high technical bar with your team. Our current stack includes Python 3.11+ with FastAPI, React 18+ with TypeScript, and pydantic.ai for LLM-powered components; we expect this leader to be comfortable working in that stack and to evaluate, evolve, and own those standards going forward
- Demonstrated ability to collaborate across organizational boundaries — this role operates in tight partnership with Enterprise Apps, Data, Product Development, and Infosec teams. You have a track record of building shared architectural contracts
- Experience evaluating and selecting between build, buy, and assemble options at the layer level — including vendor assessment, total cost of ownership modeling, and the discipline to not over-engineer where mature open-source or commercial components already exist
- 7+ years of software engineering experience, with at least 3 years in an engineering leadership role and at least 1 year leading a team building AI or ML infrastructure in a production enterprise environment
Benefits
- Restricted Stock Units (RSUs)
- Employee Stock Purchase Plan (ESPP)
- Flexible time off
- Paid company holidays and paid sick time
- Gender-neutral parental leave
- Grandparent leave
- Medical, dental, and vision coverage
- 401(k) retirement plan with company match
- Life and disability insurance
- Health and dependent care FSA
- Voluntary benefits (hospital, accident, critical illness)
- Employee Assistance Program (EAP)
- ARAG pre-paid legal
- Nationwide pet insurance
- Cancer Care program
- Global business travel medical insurance
- Home office allowance
- Mobile phone reimbursement
- Wellness coach
- Wellness/gym reimbursement
- Fertility coverage
- Adoption & surrogacy reimbursement
Company Overview