Senior Director, Enterprise AI Platform Engineering

This is a fully remote position, open to applicants in United States.

📋 Description

• Take ownership and implement the strategy for SentinelOne's enterprise AI platform — encompassing the Gateway, Harness, and Semantic Layers — transitioning from direct-to-LLM connections to a governed, multi-model control plane.

• Develop and establish the AI Gateway layer, which includes centralized identity management and authentication/authorization, token budgeting, data loss prevention (DLP) and content guardrails, multi-model routing and failover, MCP allow-listing, and a comprehensive audit trail for every AI request.

• Construct and manage the Harness layer, featuring agent orchestration (LangGraph or a similar tool), prompt formulation and context management, memory and state management across multi-turn interactions, and model abstraction across providers like Anthropic, OpenAI, Google, and others through AWS Bedrock and Vertex, including MCP/tool integration for internal and SaaS-embedded agents.

• Oversee the technical roadmap for SentinelOne's Claude & Gemini Enterprise plugin framework — a developing repository of role-based skills and agents that deliver AI capabilities to employees contextualized to their specific roles. This involves broadening coverage across various roles and use cases, enhancing the secure authoring, testing, and deployment processes for plugins, and continuously refining the quality and reliability of each plugin's output.

• Collaborate closely with teams in Enterprise Data, Enterprise Apps, Product Development, and Infosec to connect the platform to regulated data sources, align on security measures, and guarantee that every integration adheres to shared architectural agreements.

• Regulate the interaction protocols for agents — whether user-initiated within Anthropic, Google, or OpenAI products, engineered by SentinelOne teams, embedded in SaaS applications, or sourced from external partners — ensuring that all requests comply with the same policy controls regardless of their origin.

• Recruit, mentor, and provide technical leadership to a team of engineers; establish standards, review architectural choices, and foster an environment where top engineers can excel.

• Clearly communicate platform status, architectural decisions, and risk assessments to executive stakeholders.

⛳️ Requirements

• You have developed at least one production platform — whether AI-related or not — that enforced centralized policy across multiple consumers, including authentication/authorization, rate limiting, observability, and audit logging. Candidates with experience in AI platforms (LLM gateway, model routing, or orchestration) are preferred, but strong applicants from API, data, or developer platforms are encouraged to apply. It's important that you can articulate what you built, the tradeoffs involved, and what you might do differently.

• In-depth hands-on experience with agent orchestration (LangGraph or an equivalent) is essential, along with familiarity in routing to hosted model providers such as AWS Bedrock and Google Vertex. You grasp the functioning of context windows, memory, and tool-calling in a practical context.

• Proven experience in designing and implementing an AI gateway — including centralized authentication, rate limiting, DLP, observability, and audit logging across various model providers. Familiarity with Kong AI Gateway is an added advantage.

• You possess experience in building or managing a model evaluation framework — not merely executing benchmarks, but also utilizing evaluation results to inform model selection, prompt tuning, and routing strategies.

• Acquaintance with the Model Context Protocol (MCP) and hands-on experience managing tool access for agents across enterprise systems securely is required.

• Practical knowledge of enterprise AI deployments — particularly Claude Enterprise (Anthropic) and Gemini Enterprise — is crucial, including administration, access and policy control mechanisms, and their connections to a broader governed AI architecture.

• A strong software engineering foundation is necessary — you should be capable of writing and reviewing production code, understanding API design, and maintaining a high technical standard within your team. Our current technology stack includes Python 3.11+ with FastAPI, React 18+ with TypeScript, and pydantic.ai for LLM-powered components; we expect this leader to be adept in this stack and to evaluate, evolve, and uphold these standards moving forward.

• Demonstrated ability to collaborate across organizational boundaries is essential — this role involves close partnerships with teams in Enterprise Apps, Data, Product Development, and Infosec. You have a proven track record of establishing shared architectural agreements.

• Experience in assessing and choosing between build, buy, and assemble options at the layer level is important, including vendor evaluation, total cost of ownership analysis, and the discipline to avoid over-engineering where robust open-source or commercial solutions already exist.

• A minimum of 7 years of software engineering experience is required, with at least 3 years in a leadership capacity and at least 1 year leading a team focused on building AI or ML infrastructure within a production enterprise setting.

🏝️ 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