2 months ago
Base Salary
$186k - $282k/yr
Responsibilities
- Own the AWS Bedrock and Bedrock AgentCore footprint across US, EU, and AU, including model access, throughput, cross-region inference, quotas, throttles, and availability.
- Operate sandboxed execution environments for AI-generated code with session limits, network controls, and least-privilege IAM.
- Write and review Terraform for a multi-account, multi-region AWS estate.
- Build AI workload observability covering token consumption, latency, throttles, retries, tool-call failures, sandbox outcomes, generation success, and agent traces.
- Define journey-based SLOs and operate AI-specific reliability and on-call runbooks.
- Engineer AI cost attribution and controls across inference, serving, sandbox compute, and telemetry.
- Build GitHub Actions pipelines for Node/TypeScript and Python services, with versioned, evaluated, feature-flagged, and reversible model and prompt changes.
- Plan capacity for peak accounting workloads and enforce tenant isolation, audit logging, encryption, patching, and asset inventory.
- Inventory and instrument the AI runtime, eliminate infrastructure drift, establish cost baselines, and document reusable runtime patterns.
Requirements
- 5+ years of experience in DevOps, SRE, platform, or infrastructure engineering, including production systems supported through on-call.
- Deep 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 infrastructure.
- CI/CD and container ownership experience, including GitHub Actions, Docker, image supply chains, and scaling policies.
- Production experience operating an LLM-backed or ML-serving workload, with concrete knowledge of tokens, latency, throttling, and cost.
- Experience with observability tools such as Grafana, Prometheus, or OpenTelemetry, distributed tracing, and user-experience-focused SLOs.
- Working fluency in Python or TypeScript/Node.js and ability to read and contribute to services in the other.
- Preferred experience includes multi-region data-residency infrastructure, AI cost and performance optimization, sandboxed untrusted-code execution, Terraform orchestration, monorepo build systems, progressive delivery, FinOps, data infrastructure, audit evidence, and regulated SaaS.
- Model training, fine-tuning, research publications, accounting experience, and a PhD or formal ML credential are not required.
Benefits
- Medical, dental, vision, family-forming benefits, life and disability insurance, unlimited vacation, and participation in an employee stock program.
- The position has a stated base pay range of $186,000-$282,000 per year.
- The interview process includes a 30-minute recruiter screen followed by approximately four hours of hiring manager, technical, design, and team-panel conversations.
Tech Stack
Apache SparkAWSBazelDockerGitHub ActionsGrafanaHarnessMongoDBNode.jsPostgreSQLPrometheusPythonSnowflakeTerraformTypeScript
Categories
About FloQast
FloQast builds an accounting close and reconciliation platform for corporate finance teams, delivered as SaaS with AI-enabled workflow automation and compliance tools. It integrates with ERPs like NetSuite, Sage Intacct, and SAP to streamline month-end close, reconciliations, and reporting; notable customers include Lululemon, Chipotle, and Shopify. Founded in 2013 and headquartered in Sherman Oaks, California, the company is privately held.