[Hiring] Senior AI Platform Engineer, Infrastructure Services @SentinelOne

Role Description

As a Senior AI Platform Engineer, Infrastructure Services, you will be tasked with taking ownership of our AI Gateway infrastructure (built on Kong AI Gateway), the system that authenticates, routes, rate-limits, and monitors AI coding assistant traffic org-wide.

This is a high-autonomy, high-scope role: you will set technical direction for AI infrastructure, drive incident response and reliability work, and partner closely with the engineers who own our CI/CD, GitOps, and artifact systems rather than working in isolation from them.

What Will You Do?

• 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.
• 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.
• 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).
• Track usage and cost: build observability into token usage, latency, and spend across both API-based and self-hosted models.

Qualifications

• 5 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 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.
• 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 and GPU-backed infrastructure.
• Familiarity with LLMOps practices: model versioning, evaluation harnesses, and usage/cost observability.
• Track record of setting technical direction, driving cross-team initiatives, and mentoring other engineers.
• 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 inside an enterprise is preferred.
• Familiarity with Okta/OIDC and enterprise auth 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 RAG pipelines in a production setting is preferred.
• Exposure to model fine-tuning or lightweight training pipelines for domain-specific model adaptation is preferred.

Benefits

• Equity & Rewards: Restricted Stock Units (RSUs), Employee Stock Purchase Plan (ESPP).
• Time Off & Wellbeing: Flexible time off, Paid company holidays and paid sick time, Gender-neutral parental leave, Grandparent leave.
• Insurance & Financial Security: Medical, dental, and vision coverage, 401(k) retirement plan with company match, Life and disability insurance, Health and dependent care FSA, Voluntary benefits, Employee Assistance Program (EAP), ARAG pre-paid legal, Nationwide pet insurance, Cancer Care program, Global business travel medical insurance.
• Work Perks & Flexibility: Home office allowance, Mobile phone reimbursement.
• Wellness & Lifestyle: Wellness coach, Wellness/gym reimbursement, Fertility coverage, Adoption & surrogacy reimbursement.