# Senior DevOps Engineer, AI Platform

**Company:** [FloQast](https://hotfix.jobs/companies/floqast)
**Location:** San Jose, CA
**Role:** DevOps / SRE
**Experience:** 5+ years
**Skills:** AWS, Aws Bedrock, Aws Bedrock Agentcore, Terraform, Ecs/Fargate, AWS Lambda, Amazon Sqs, Amazon S3, IAM, Docker, GitHub Actions, Grafana, Prometheus, OpenTelemetry, Python
**Posted:** 2026-08-24

> Owns the multi-region AI runtime and platform reliability for customer-facing products, including Bedrock infrastructure, sandboxed execution, observability, cost controls, Terraform, and delivery pipelines. Requires 5+ years in DevOps, SRE, platform, or infrastructure engineering plus production experience operating LLM-backed workloads.

## Job Description

## Responsibilities
- Own AWS Bedrock and Bedrock AgentCore infrastructure across US, EU, and AU regions, including model access, throughput, cross-region inference, quotas, throttling, retries, budgeting, logging, and tracing.
- Operate sandboxed execution environments for AI-generated code with session limits, network controls, tenant isolation, and least-privilege IAM.
- Develop and review Terraform for multi-account, multi-region AWS infrastructure.
- Extend Grafana observability for token consumption, model and regional latency, throttling, retries, tool-call failures, sandbox outcomes, generation success, and agent traces.
- Define journey-based SLOs and runbooks for AI-specific failure modes.
- Manage AI cost attribution across inference, serving, sandbox compute, and telemetry.
- Build GitHub Actions CI/CD pipelines for Node.js/TypeScript and Python services in NX monorepos.
- Gate, version, feature-flag, and roll back model and prompt changes using evaluation and regression suites.
- Participate in DevOps on-call and plan capacity around peak accounting periods.
- Maintain SOC 2 and ISO 27001/42001 controls, including audit logging, encryption, access control, patching, asset inventory, and tenant isolation.

## Requirements
- 5+ years of experience in DevOps, SRE, platform, or infrastructure engineering, including production on-call responsibility.
- Hands-on AWS experience with ECS/Fargate, Lambda, SQS, S3, IAM, VPC, networking, and ALB/NLB.
- Production-scale Terraform experience with modules, state management, multi-region, and multi-account deployments.
- Experience with CI/CD, Docker, image supply chains, and scaling policies; GitHub Actions preferred.
- Production operation of an LLM-backed or ML-serving workload, including tokens, latency, throttling, and cost.
- Experience with observability tools and practices such as Grafana, Prometheus, OpenTelemetry, distributed tracing, and user-experience SLOs.
- Working fluency in Python or TypeScript/Node.js, with the ability to read and debug the other.

## Nice to have
- Multi-region infrastructure under data-residency constraints.
- AI cost and performance optimization, including token accounting, prompt caching, batching, model routing, and capacity right-sizing.
- Sandboxed execution of untrusted or generated code.
- Atmos or comparable Terraform orchestration and NX, Turborepo, or Bazel.
- Progressive delivery with Harness or similar feature flags.
- FinOps tooling such as CloudZero and per-tenant cost attribution.
- MongoDB, PostgreSQL, Snowflake, or EMR/Spark.
- SOC 2 or ISO 27001 audit evidence, cloud-cost reduction, or regulated SaaS experience.

## Compensation and benefits
- The role includes on-call responsibilities and work supporting customer-facing AI products across multiple regions.
- A PhD or formal ML credential is not required.

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