Staff AI Platform Engineer, Infrastructure Services

Details

SentinelOne is seeking a Staff AI Platform Engineer, Infrastructure Services, to own its AI Gateway infrastructure, built on Kong AI Gateway. The platform authenticates, routes, rate-limits, and monitors AI coding assistant traffic across the organization. This high-autonomy role will set technical direction for AI infrastructure, lead incident response and reliability efforts, and design solutions spanning CI/CD, GitOps, Kubernetes, and artifact systems.

Responsibilities

• Architect, harden, and scale the Kong AI Gateway deployment using Konnect Hybrid on KCP/EKS.
• Manage authentication with Okta/OIDC, consumer tiers and budgets, rate limiting, semantic caching, and observability.
• Lead root-cause analysis and remediation for gateway issues involving timeouts, latency, capacity, and failover.
• Build monitoring and alerting to identify issues before they affect users.
• Design solutions across the broader platform, including Jenkins, JPAAS, ArgoCD, Kubernetes, Artifactory, Xray, GitHub Enterprise, and GitHub Actions runner fleets.
• Evaluate and deploy AI developer tooling, including AI-assisted pull request review tools such as Qodo and engineering metrics platforms such as LinearB.
• Make build-versus-buy recommendations for AI developer tooling.
• Define architecture and standards for AI infrastructure, review team designs, and mentor engineers.
• Partner with security, Developer Experience, and product engineering teams to translate platform requirements into capabilities.
• Deploy and operate self-hosted and open-weight model serving infrastructure using technologies such as vLLM, NVIDIA Triton/NIM, TGI, and Ollama.
• Plan GPU capacity, configure autoscaling, and tune cost and performance.
• Help establish LLMOps practices covering model versioning, evaluation, safe rollout, retrieval-augmented generation, vector stores, and embedding pipelines.
• Build observability for token usage, latency, and spending across API-based and self-hosted models.

Requirements

• At least 8 years of experience in platform, infrastructure, or DevOps engineering, with a history of owning production systems end to end.
• Hands-on experience with API gateway technologies such as Kong, Envoy, Apigee, or similar.
• Experience with AI or LLM gateway patterns, including rate limiting, semantic caching, and prompt/response observability, is preferred.
• Strong Kubernetes and GitOps experience, including ArgoCD or a comparable platform.
• Experience operating across development, government, and production environments.
• Strong CI/CD experience, including Jenkins pipeline design and administration, build infrastructure, and runner or agent fleet management.
• Experience with artifact and package management systems such as Artifactory and Xray, or similar tools.
• Experience administering source control platforms such as GitHub Enterprise.
• Working knowledge of 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.
• Experience setting technical direction, leading cross-team initiatives, and mentoring engineers.
• Clear and proactive communication skills, with the ability to explain infrastructure trade-offs to technical and non-technical stakeholders.
• Enterprise experience operating LLM- or AI-assisted developer tools such as Claude Code or Copilot is preferred.
• Familiarity with Okta/OIDC and enterprise authentication patterns is preferred.
• Experience with engineering productivity metrics tools such as LinearB and AI-based code review tools such as Qodo is preferred.
• Production experience with vector databases and RAG pipelines, such as Milvus, Pinecone, or pgvector, is preferred.
• Exposure to model fine-tuning or lightweight training pipelines, such as LoRA or QLoRA, is preferred.

Benefits

• Restricted Stock Units and Employee Stock Purchase Plan.
• Flexible time off, paid company holidays, paid sick time, gender-neutral parental leave, and grandparent leave.
• Medical, dental, and vision coverage.
• 401(k) retirement plan with company match.
• Life and disability insurance.
• Health and dependent care FSA.
• Voluntary hospital, accident, and critical illness benefits.
• Employee Assistance Program and ARAG pre-paid legal services.
• Nationwide pet insurance and Cancer Care program.
• Global business travel medical insurance.
• Home office allowance and mobile phone reimbursement.
• Wellness coach, wellness/gym reimbursement, fertility coverage, and adoption and surrogacy reimbursement.

Compensation

The U.S. base salary range is $156,000–$215,000 per year and may vary based on the candidate's location. Different pay ranges may apply in some locations.