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SentinelOne

Builds security software for enterprise systems

Senior Staff AI Platform Engineer - Infrastructure Services

Platform EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USARemote

About the Job

Requirements

  • At least 8 years of experience in platform, infrastructure, or DevOps engineering, with a track record of owning production systems end-to-end.
  • Hands-on experience with API gateway technologies such as Kong, Envoy, or Apigee; direct experience with AI/LLM gateway patterns is a plus.
  • Strong Kubernetes and GitOps experience, such as ArgoCD, and comfort operating across development, government, and production environments.
  • Solid CI/CD background, including Jenkins pipeline design and administration, build infrastructure, and runner or agent fleet management.
  • Experience with artifact and package management systems such as Artifactory or Xray, and source control platform administration such as GitHub Enterprise.
  • Working knowledge of infrastructure as code, such as Terraform, and cloud platforms such as AWS/EKS.
  • Experience deploying and operating self-hosted LLM inference stacks and GPU-backed infrastructure, including Kubernetes GPU scheduling and autoscaling.
  • Familiarity with LLMOps practices, including model versioning, evaluation harnesses, and usage and cost observability across API-based and self-hosted models.
  • Track record of setting technical direction, driving cross-team initiatives, and mentoring other engineers; the role has significant scope and minimal day-to-day oversight.
  • Experience operating LLM/AI-assisted developer tooling at enterprise scale is preferred.
  • Familiarity with Okta/OIDC and enterprise authentication patterns for internal platforms is preferred.
  • Experience with engineering productivity metrics tooling and AI-based code review tooling is preferred.
  • Experience with vector databases and retrieval-augmented generation (RAG) pipelines in a production setting is preferred.
  • Exposure to model fine-tuning or lightweight training pipelines such as LoRA/QLoRA for domain-specific model adaptation is preferred.

Responsibilities

  • Architect, harden, and scale the Kong AI Gateway deployment, including authentication, consumer tiers and budgets, rate limiting, semantic caching, and observability.
  • Drive root-cause analysis and remediation for gateway incidents, including timeouts, latency, capacity, and failover, and build monitoring and alerting to detect issues proactively.
  • Work across CI/CD, GitOps and Kubernetes deployment tooling, artifact management, GitHub Enterprise administration, and the GitHub Actions runner fleet so AI infrastructure decisions account for the broader platform.
  • Run structured pilots and adoption efforts for AI developer tools such as AI-assisted pull request review and engineering metrics platforms, and make build-versus-buy recommendations.
  • Define AI infrastructure architecture and standards, review designs across the team, and mentor engineers working in this space.
  • Partner with security, DevEx, and product engineering teams consuming the gateway to translate their needs into platform capabilities.
  • Stand up and operate self-hosted, open-weight model-serving infrastructure, including GPU capacity planning, autoscaling, and cost and performance tuning.
  • Help build model lifecycle practices such as model versioning, evaluation, and safe rollout, plus supporting infrastructure for retrieval-augmented generation, vector stores, and embedding pipelines as use cases mature.
  • Build observability into token usage, latency, and spend across API-based and self-hosted models.

Desired Qualifications

  • Direct experience with AI/LLM gateway patterns such as rate limiting, semantic caching, and prompt/response observability.
  • Experience operating LLM/AI-assisted developer tooling such as Claude Code or Copilot at enterprise scale.
  • Familiarity with Okta/OIDC and enterprise authentication patterns for internal platforms.
  • Experience with engineering productivity metrics tooling such as LinearB and AI-based code review tooling such as Qodo.
  • Experience with vector databases such as Milvus, Pinecone, or pgvector and production retrieval-augmented generation pipelines.
  • Exposure to model fine-tuning or lightweight training pipelines such as LoRA/QLoRA for domain-specific model adaptation.

About the Company

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SentinelOne

Builds security software for enterprise systems

SentinelOne builds cybersecurity software for organizations that need to protect computers, cloud workloads, user identities and business use of AI. Its Singularity platform combines threat prevention, detection and response with shared security data so teams can monitor different parts of their environment from one console. Software agents watch activity on protected systems and can isolate threats, while cloud and identity tools extend coverage beyond employee devices. The company sells platform subscriptions and optional modules, priced mainly by protected agents, and supplies managed threat hunting and incident response services. SentinelOne was founded in 2013 and is headquartered in Mountain View, California.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Mountain View, CA
Founded
2013

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