Senior AI Platform Reliability Engineer

FloQast; San Jose (CA); Hybrid; USD 186,000 - 282,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Family forming benefits

Life & Disability Insurance

Unlimited vacation

Employee stock program

Job summary

FloQast is seeking an experienced DevOps/AI infrastructure engineer to own the AI runtime across US, EU, and AU, including Bedrock and sandboxed code execution. You’ll implement IaC, throughput control, and cost attribution for multi-region AI workloads.

The role emphasizes observability, security, and reliability, with on-call responsibilities and a focus on scalable, auditable infrastructure in a Production AI environment.

Qualifications

• 5+ years in DevOps, SRE, platform, or infrastructure engineering with on-call experience.
• Deep, hands-on AWS: ECS/Fargate, Lambda, SQS, S3, IAM, VPC and networking, ALB/NLB.
• Terraform at production scale: modules, state management, multi-region, multi-account.
• CI/CD and container ownership: GitHub Actions, Docker, image supply chain, scaling policies.
• Production AI infrastructure: operated LLM-backed or ML-serving workloads in production.

Responsibilities

• Own AI runtime infrastructure across US, EU, AU regions (Bedrock and AgentCore).
• Operate sandboxed execution environments for AI-generated code.
• Write and review Terraform across multi-account AWS estate.
• Extend Grafana observability for AI workloads (signals, latency, token spend).
• Manage AI spend as a cost line with tagging for FinOps.

Skills

DevOps/SRE experience

AWS expertise

Terraform at scale

CI/CD engineering

Python/TypeScript

Observability tooling

Tools

GitHub Actions

Docker

Terraform

NX monorepos

Grafana

Job description

FloQast is seeking an experienced DevOps/AI infrastructure engineer to own the AI runtime across US, EU, and AU, including Bedrock and sandboxed code execution. You’ll implement IaC, throughput control, and cost attribution for multi-region AI workloads.

The role emphasizes observability, security, and reliability, with on-call responsibilities and a focus on scalable, auditable infrastructure in a Production AI environment.