Note: The job is a remote job and is open to candidates in USA. SentinelOne is a company at the intersection of AI and security, pioneering a new operating model for cybersecurity. As a Staff AI Platform Engineer, Infrastructure Services, you will take ownership of the AI Gateway infrastructure, set technical direction for AI infrastructure, and partner closely with various engineering teams to design and implement solutions.
Responsibilities
- Work on the AI Gateway platform: architect, harden, and scale our Kong AI Gateway deployment (Konnect Hybrid on KCP/EKS), including auth (Okta/OIDC), consumer tiers and budgets, rate limiting, semantic caching, and observability
- Lead reliability and incident response: drive root-cause analysis and remediation for gateway issues (timeouts, latency, capacity, failover) and build the monitoring/alerting needed to catch them before users do
- Design across the platform, not just the gateway: work fluently with our CI/CD (Jenkins, JPAAS), GitOps and Kubernetes deployment tooling (ArgoCD across dev/gov/prod), artifact management (Artifactory/Xray), GitHub Enterprise administration, and GitHub Actions runner fleet, so that AI infrastructure decisions account for how the rest of the platform actually works
- Evaluate and roll out AI developer tooling: run structured pilots and adoption efforts for tools like AI-assisted PR review (Qodo) and engineering metrics platforms (LinearB), and make clear build-vs-buy recommendations
- Set technical direction and mentor: define architecture and standards for AI infrastructure, review designs across the team, and raise the bar for other engineers working in this space
- Partner cross-functionally: work directly with security, DevEx, and product engineering teams consuming the gateway to translate their needs into platform capabilities
- Host and serve local models: stand up and operate self-hosted/open-weight model serving infrastructure (e.g. vLLM, NVIDIA Triton/NIM, TGI, Ollama) for workloads where routing to an external provider isn't the right fit, including GPU capacity planning, autoscaling, and cost/performance tuning
- Support the broader model lifecycle: help build LLMOps practices such as model versioning, evaluation, and safe rollout, plus supporting infrastructure for retrieval-augmented generation (vector stores, embedding pipelines) as use cases mature
- Track usage and cost: build observability into token usage, latency, and spend across both API-based and self-hosted models so the business can see what AI infrastructure actually costs
Skills
- 8 or more years of experience in platform, infrastructure, or DevOps engineering, with a track record of owning systems end-to-end in production
- Hands-on experience with API gateway technologies (Kong, Envoy, Apigee, or similar); direct experience with AI/LLM gateway patterns (rate limiting, semantic caching, prompt/response observability) is a strong plus
- Strong Kubernetes and GitOps experience (ArgoCD or comparable), and comfort operating across multiple environments (dev, gov, prod)
- Solid CI/CD background: Jenkins pipeline design and administration, build infrastructure, and runner/agent fleet management (GitHub Actions runners or equivalent)
- Experience with artifact and package management systems (Artifactory, Xray, or similar) and source control platform administration (GitHub Enterprise)
- Working knowledge of infrastructure-as-code (Terraform) and cloud platforms (AWS/EKS)
- Experience deploying and operating self-hosted LLM inference stacks (vLLM, NVIDIA Triton/NIM, TGI, Ollama, or similar) and GPU-backed infrastructure, including Kubernetes GPU scheduling and autoscaling
- Familiarity with LLMOps practices: model versioning, evaluation harnesses, and usage/cost observability across API-based and self-hosted models
- Track record of setting technical direction, driving cross-team initiatives, and mentoring other engineers; this role has significant scope and minimal day-to-day oversight
- Clear, proactive communicator who can explain infrastructure trade-offs to both engineers and non-technical stakeholders
- Experience operating LLM/AI-assisted developer tooling at scale (Claude Code, Copilot, or similar) inside an enterprise is preferred
- Familiarity with Okta/OIDC and enterprise auth patterns for internal platforms is preferred
- Experience with engineering productivity metrics tooling (LinearB or similar) and AI-based code review tooling (Qodo or similar) is preferred
- Experience with vector databases and RAG pipelines (e.g. Milvus, Pinecone, pgvector, or similar) in a production setting is preferred
- Exposure to model fine-tuning or lightweight training pipelines (LoRA/QLoRA or similar) for domain-specific model adaptation is preferred
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
Company Overview