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 Staff AI Platform Engineer, you will be a hands-on builder of SentinelOne’s enterprise AI platform, responsible for integrating, deploying, and operating the foundational services that enable secure, scalable, and reliable AI adoption across the company.

This role sits at the intersection of AI infrastructure & Platform Engineering, DevOps, and software development . You will work with AI Gateways, Model Context Protocol (MCP) services, model providers, evaluation frameworks, and enterprise integrations to deliver production-grade AI platform capabilities.

We are looking for an experienced engineer who combines strong cloud-native infrastructure knowledge with hands-on DevOps expertise, proficiency in Python, and demonstrated experience integrating AI technologies into working systems.

You should be comfortable independently deploying, configuring, troubleshooting, and operating AI platform components on existing cloud infrastructure, while collaborating with infrastructure engineers on the underlying environment.

Working closely with the Sr. Director of Enterprise AI Platform Engineering and cross-functional teams, you will translate platform architecture into working solutions and take ownership of technical deliverables from design through production operations.

What Will You Do?

Primary responsibilities include:

• Build and operate AI Gateway capabilities: Deploy, configure, integrate, and maintain enterprise AI Gateway services using technologies such as Kong AI Gateway, LiteLLM, or comparable platforms. Implement model connectivity, multi-model routing, failover, authentication, authorization, rate limiting, token consumption controls, and request-level observability.

• Build and manage MCP integrations: Integrate first-party and third-party MCP servers with enterprise applications, AI agents, and platform services. Implement secure tool access, authentication, authorization, and governance controls for agent-to-tool interactions.

• Develop and operate AI platform infrastructure: Configure, deploy, and maintain AI platform services running on cloud-native infrastructure, with a focus on service connectivity, security, scalability, resilience, and operational reliability.

• Own deployment and DevOps practices: Independently deploy and operate production platform services using containerization, Kubernetes, CI/CD pipelines, and deployment automation. Diagnose and resolve application, deployment, configuration, and infrastructure-related issues.

• Develop Python-based platform services: Build production-grade Python services, APIs, and reusable integration components using frameworks such as FastAPI and other modern development tools.

• Integrate AI models and enterprise systems: Connect hosted AI model providers, internal applications, enterprise APIs, identity systems, and data services through secure and maintainable integration patterns.

• Implement AI evaluation and observability capabilities: Integrate evaluation frameworks, tracing, logging, and monitoring into AI workflows to assess model behavior, tool execution, reliability, and platform performance.

• Implement enterprise AI security and governance: Support centralized access controls, identity integration, policy enforcement, content guardrails, audit logging, and AI consumption governance across shared platform capabilities.

• Engineer for production reliability: Troubleshoot complex issues across AI services, gateways, integrations, and deployment environments. Implement appropriate testing, diagnostics, performance monitoring, and operational controls.

• Collaborate on platform architecture and execution: Partner with infrastructure, enterprise applications, data engineering, and security teams to translate architectural requirements into scalable, secure, maintainable production services.

What Skills and Knowledge Will You Bring?

Ideal candidates will have:

• AI Gateway and API Gateway knowledge: Practical experience with API gateways, AI gateways, or comparable centralized service access platforms. Understanding of model routing, authentication, authorization, traffic management, rate limiting, policy enforcement, observability, and gateway operations. Hands-on experience with Kong AI Gateway, LiteLLM, or similar technologies is a strong plus.

• Strong cloud-native platform engineering experience: Hands-on experience configuring, deploying, integrating, and operating distributed services on cloud infrastructure, with practical knowledge of Kubernetes, containerization, networking, service connectivity, scalability, and reliability. Experience with GCP and GKE or comparable cloud environments is a plus.

• Strong DevOps and production operations experience: Demonstrated ability to independently deploy, monitor, troubleshoot, and maintain production services. Experience with CI/CD, deployment automation, infrastructure-as-code practices, and application observability.

• Proficiency in Python: Hands-on experience developing production-grade Python services and APIs. Experience with FastAPI or equivalent frameworks is desirable.

• AI evaluation and observability knowledge: Working understanding of AI evaluation approaches, model and agent behavior assessment, tracing, monitoring, and operational diagnostics. Experience with tools such as Logfire, LangSmith, or comparable platforms is a plus.

• Demonstrated AI integration experience: Hands-on experience integrating LLMs, model providers, AI services, agent tools, or AI-enabled applications into working systems. Experience with Anthropic, OpenAI, AWS Bedrock, or Google Vertex AI is valuable.

• MCP and agent integration knowledge: Familiarity with Model Context Protocol (MCP), AI tool calling, agent-to-tool connectivity, and securely integrating AI agents with enterprise services. Experience with Pydantic AI, LangGraph, or comparable agent frameworks is desirable.

• Enterprise integration, security, and identity: Experience integrating enterprise applications, APIs, cloud services, and identity systems using secure, maintainable integration patterns. Strong understanding of authentication, authorization, OAuth 2.0, OpenID Connect, service-to-service communication, and API security. Familiarity with enterprise identity providers such as Okta is valuable.

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