Why Cast AI?

Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production.

The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation.

Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS.

Global team, diverse perspectives
We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead.

Unicorn momentum
In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started.

Join us as we build the future of autonomous infrastructure.

About the role

Cast AI’s Sales Engineering team operates across four regions, demoing and proving out Kubernetes cost optimization and autoscaling in real customer environments. Two problems compound as we scale:

Our tooling lives on individual laptops. Demo environments, POC scaffolding, and provisioning scripts are bespoke, undocumented, and fragile. When they break, deals stall.
Releases reach customers before they have been stress-tested the way the field actually uses them. Internal QA validates against clean, controlled clusters. Customers run messy, multi-cloud, spot-heavy, oddly configured ones, and that is where things break, often during a live POC.

This person fixes both. They own the SE platform end to end, and they act as the field-representative quality gate: deliberately breaking releases the way real customer clusters break, before customers do, and feeding that signal back into product quality.
This role sits in Sales Engineering by design. SE has direct commercial accountability (a broken release kills a live deal) and sees deployment patterns internal QA never reproduces. That independence is the point.

What success looks like:

First 90 days: Audit the current SE tooling sprawl, pick the two highest-pain demo and POC flows, and move them fully into CI/CD with zero local dependencies. Map the current release process and propose where the SE quality gate plugs in.
6 months: The SE team provisions all standard demo and POC environments from pipelines. A documented field-quality gate is live in the release process, with at least one prevented field-facing regression to point to.
12 months: The SE platform is self-service and reproducible. The field-quality signal is a trusted input that product and engineering actively pull from before GA.

What this role is not:

Not a customer-facing Sales Engineer: minimal demos, no quota, no deal-carrying.
Not part of the core engineering QA org. You are the field’s voice, deliberately independent.
Not a maintenance role. You are building the function, not keeping someone else’s lights on.

How this role works
You report into Sales Engineering, and your quality findings carry weight through a formal release gate and a direct partnership with engineering and QA leadership. You have the independence of the field’s perspective and the authority to stop a release that would break in front of a customer. That combination of platform builder and field-representative quality owner is rare, and it is exactly the point.

Requirements:

• Deep, hands-on Kubernetes expertise, not surface familiarity. CKA or CKAD is the gold standard here.
• Strong CI/CD engineering: you have built non-trivial pipelines in GitLab CI and/or GitHub Actions from scratch, not just maintained existing ones.
• Infrastructure as code: Terraform, Helm, containerization. You version and peer-review infrastructure rather than treating it as a snowflake.
• A genuine QA and “break it” instinct. You think in edge cases, failure modes, and what happens when a customer does the thing they should not.
• Multi-cloud comfort (AWS, GCP, or Azure, ideally more than one).
• Scripting fluency (Python, Go, or Bash) sufficient to build internal tooling, not just glue.
• Self-direction. This is a founding-the-function hire. You will define the playbook, not inherit one.
  
  Nice to have
  
  
• Prior experience as an SDET, platform engineer, or SRE who later moved toward quality and tooling.
• Exposure to FinOps or Kubernetes cost optimization, which is Cast AI’s domain.
• Experience standing up demo or sandbox environments for a technical sales or solutions org.
• Observability tooling (Prometheus, Grafana) and an eye for cost guardrails on ephemeral infrastructure.

Responsibilities:

• ⁠SE platform & tooling (build from scratch)
  
  Rebuild all SE tooling, including demo environments, POC provisioning, and sandbox clusters, as reproducible CI/CD pipelines in GitLab CI and/or GitHub Actions.
  Eliminate local dependencies entirely. Anyone on the SE team should be able to spin up a clean, branded, working demo from a pipeline, not a laptop.
  Treat SE infrastructure as code: Terraform/Helm, versioned, peer-reviewed, disposable and re-creatable on demand.
  Build the internal tooling the SE team does not know it needs yet: provisioning, environment teardown, cost guardrails on demo clusters, repeatable scenario seeding.
  
  
• Field quality gate (break things before customers do)
  
  Validate release candidates from the customer-deployment lens: unusual cluster topologies, multi-cloud (AWS/GCP/Azure), spot and preemptible mixes, multi-region, restrictive RBAC, air-gapped patterns, and scale edges.
  Own a defined sign-off in the release process: a documented gate that release candidates pass before GA, with the authority to flag a blocker and escalate.
  Build the automated test scenarios that replicate how the field exercises the product, so this gate scales beyond one person manually clicking around.
  
  
• Product quality feedback loop
  
  Turn field-found defects into structured, reproducible, actionable signal for product and engineering, prioritized and tied to customer and deal impact.
  Maintain a strong working relationship with engineering and QA leadership so findings land and get fixed. You do not report into them, but you partner closely with them.

What’s in it for you:

• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology.
• Enjoy a flexible, hybrid work environment.
• Equity options.
• Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.

Hiring process

• Screening call with Recruiter
• Hiring Manager interview
• 1-2 additional interviews based on the role
• Culture Check interview with an executive

*As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr.
*Please note that Cast AI does not provide any form of visa sponsorship/work permit.

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