Senior Director, AI Enterprise Platform Engineering

📍 United States

USD 198,800-298,000 per year

SENIOR

✅ Remote

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 1 API @ 1 AWS @ 4 Audit @ 4 FastAPI @ 6 LLM @ 1 Leadership @ 7 Machine Learning Observability @ 4 Python @ 6 React @ 6 Security TypeScript @ 6

Details

SentinelOne is seeking a senior engineering leader to build the enterprise AI platform that supports internal AI use cases. The platform will serve as the control plane through which AI activity is governed, observed, secured, and scaled across custom-developed AI applications and vendor-deployed solutions such as Claude Enterprise and Gemini Enterprise.

Responsibilities

Own and execute the roadmap for SentinelOne's enterprise AI platform, including the Gateway, Harness, and Semantic Layers, transitioning from direct-to-LLM connections to a governed, multi-model control plane.
Design and build the AI Gateway layer with centralized identity and AuthN/Z, token budgeting, DLP and content guardrails, multi-model routing and failover, MCP allow-listing, and a complete audit trail for AI requests.
Build and operate the Harness layer, including agent orchestration using LangGraph or an equivalent technology, prompt construction, context management, memory and state across multi-turn interactions, model abstraction across Anthropic, OpenAI, Google, and other providers, and MCP/tool wiring for internal and SaaS-embedded agents.
Own the technical roadmap for the Claude and Gemini Enterprise plugin framework, including role-based skills and agents, secure plugin authoring, testing and deployment, and continuous improvement of plugin output quality and reliability.
Partner with Enterprise Data, Enterprise Apps, Product Development, and Infosec to connect the platform to governed data sources, align security controls, and establish shared architectural contracts.
Govern how agents from Anthropic, Google, OpenAI, SentinelOne teams, SaaS platforms, and external partners interact with SentinelOne systems, ensuring all callers pass through consistent policy controls.
Hire, mentor, and technically lead a team of engineers; set standards and review architecture decisions.
Communicate platform status, architecture decisions, and risk posture to executive stakeholders.

Requirements

Experience building at least one production platform that enforced centralized policy across multiple consumers, including authentication/AuthN/Z, rate limiting, observability, and audit logging. Experience with AI platforms, LLM gateways, model routing, or orchestration is a bonus; API, data, and developer platform experience is also relevant.
Hands-on experience with agent orchestration using LangGraph or an equivalent technology, including routing to hosted model providers such as AWS Bedrock and Google Vertex.
Practical understanding of context windows, memory, and tool calling.
Experience designing and implementing an AI gateway with centralized authentication, rate limiting, DLP, observability, and audit logging across multiple model providers. Experience with Kong AI Gateway is a plus.
Experience building or owning a model evaluation framework and using evaluation results to guide model selection, prompt tuning, and routing decisions.
Familiarity with the Model Context Protocol (MCP) and experience governing secure tool access for agents operating across enterprise systems.
Familiarity with enterprise AI deployments, specifically Claude Enterprise and Gemini Enterprise, including administration, access and policy controls, and integration with governed AI architectures.
Strong software engineering background, including production coding and code review experience and a strong understanding of API design.
Comfort working with Python 3.11+, FastAPI, React 18+, TypeScript, and pydantic.ai for LLM-powered components.
Experience collaborating across Enterprise Apps, Data, Product Development, and Infosec teams and building shared architectural contracts.
Experience evaluating build, buy, and assemble options, including vendor assessment and total cost of ownership modeling.
At least 7 years of software engineering experience, including 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.