The posting
Why this role exists
FloQast's AI products have outgrown the infrastructure patterns the rest of the platform runs on. Transform, AI Matching, and AutoBuilder are customer-facing products carrying real accounting workloads, and they behave nothing like a CRUD service. They call foundation models in multiple regions, execute generated code in sandboxes, spend money per token rather than per request, and fail in ways a 500-rate dashboard never catches.
Today, DevOps engineers carry this work alongside the wider fleet. We are making it someone's whole job. You will embed with the Transform and Close AI pods and own the AI runtime the way our other embedded DevOps engineers own their business unit's platform.
This is a DevOps role with an AI infrastructure specialization, not a research or modeling role. You will not train models or tune prompts for accuracy. You will make the systems that serve them fast, observable, multi-region, cost-bounded, and auditable.
The products you'll support
Transform. FloQast's data transformation and analytics product: a monorepo of containerized services on AWS, including an agentic LLM thread runtime, a natural-language-to-SQL service, and queue-driven workflow workers. You'll own the foundation-model runtime across our US, EU, and AU regions, sandbox isolation for AI-executed code, autoscaling for the worker fleet, and cost attribution for model spend.
AI Matching. The automated reconciliation matching stack in our Close product line: LLM-backed match scoring, a matching copilot service family, and a regression harness that guards matching quality. You'll own throughput and unit economics at close-cycle peak, the sandbox for AI-generated code, and keeping the eval harness running in CI so a model or prompt change cannot ship blind.
AutoBuilder. The Transform capability that generates transformation workflows and scripts for users instead of making them hand-build each one. You'll own generation-queue health and backpressure, triage that distinguishes a model failure from an infrastructure failure, scale-to-demand behavior for spiky load, and the latency budget for a user waiting on a generated artifact.
What You'll Do:
What You'll Bring:
- 5+ years in DevOps, SRE, platform, or infrastructure engineering, including production systems you were on call for.
- 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 preferred, Docker, image supply chain, scaling policies.
- Production AI infrastructure: you have operated at least one LLM-backed or ML-serving workload in production and can speak concretely about tokens, latency, throttling, and cost. Managed foundation-model APIs (Bedrock, Vertex, Azure OpenAI, Anthropic), serving platforms (SageMaker, KServe, Ray Serve, vLLM, Triton, TrueFoundry), agent or tool-execution runtimes, and eval harnesses in CI all count.
- Observability practice: Grafana, Prometheus, or OpenTelemetry, distributed tracing, and the judgment to define SLOs that measure user experience rather than server aliveness.
- Working fluency in Python or TypeScript/Node.js, at the level of reading a service, debugging it, and sending a real PR, plus the ability to read the other.
- Multi-region infrastructure under data-residency constraints (US/EU/AU). The product requires this; prior experience with it moves you to the front of the line.
- AI-specific cost and performance work: token accounting, prompt caching, batching, model routing, right-sizing serving capacity.
- Sandboxed execution of untrusted or generated code.
- Terraform orchestration layers (Atmos or comparable) and monorepo build systems (NX, Turborepo, Bazel).
- Progressive delivery with feature flags (Harness or similar).
- FinOps tooling (CloudZero or similar) and per-tenant cost attribution.
- Data infrastructure exposure: MongoDB, PostgreSQL, Snowflake, EMR/Spark.
- Audit evidence you produced for SOC 2 / ISO 27001, or a cloud-cost reduction you delivered and can explain, including what you traded to get it.
- Prior work in a regulated or audited SaaS domain: fintech, accounting, healthcare.
- Model training, fine-tuning, or research publications.
- An accounting background. We'll teach you the domain, and you'll need enough of it to know what “wrong” looks like.
- A PhD or formal ML credential. Production judgment beats coursework here.
What to Expect:
- The multi-region AI runtime is production-grade: deployed and operated across US, EU, and AU, entirely in code, with no drift attributable to AI infrastructure.
- Journey-based SLOs are live for Transform, AI Matching, and AutoBuilder, with AI-specific signals (tokens, throttles, tool-call failures, generation success) on the dashboards leadership already reads weekly.
- AI spend is attributed and bounded: per-business-unit attribution, a defensible unit-cost metric, and a delivered reduction on the AI line.
- Model and prompt changes ship behind gates: eval suites run in CI, changes are flag-controlled and reversible, and silent accuracy regressions are caught before production.
- Tenant isolation is enforced and evidenced on every AI path, and the AI stack clears its SOC 2 / ISO cycle with evidence ready.
- The runtime patterns are documented and adopted rather than rebuilt, with no single point of knowledge.
- Recruiter screen (30 min). Scope, level, location, compensation.
- Hiring manager (45 min). The AI infrastructure problem in your own words; what you've owned and what broke.
- Technical deep dive (75 min). A production incident or migration you led, interrogated properly, then hands-on: read an unfamiliar Terraform module and service, diagnose a described failure, propose the change. Use your normal tooling, including AI assistants; we care about how you verify.
- AI infrastructure design (60 min). Design the serving, scaling, observability, and cost-control path for an LLM-backed feature under multi-region data-residency constraints. Whiteboard, no coding trivia.
- Team panel (60 min). An engineering lead from Transform or Close AI, a Security or Compliance partner, and a conversation against FloQast's engineering values: Real Artists Ship; Do What Makes the Beer Taste Better; Cross the Water Then Build a Bridge; Look Before You Lock.
The base pay range for this position is $186,000- $282,000. Compensation is not limited to base salary. FloQast values our Total Rewards, and offers a competitive and elaborate Benefits Package including, but not limited to, Medical, Dental, Vision, Family Forming benefits, Life & Disability Insurance, Unlimited Vacation, and participation in our Employee Stock Program. FloQast reserves the right to amend, change, alter, and revise pay ranges and benefits offerings at any time. All applicants acknowledge that by applying to this position you understand that this specific pay range is contingent upon meeting the qualifications and requirements of the role, and for the successful completion of the interview selection and process. It is at the Company's discretion to determine what pay is provided to a candidate within the range associated with the role.
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