Senior Software Engineer | Platform Engineering

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.
This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Ireland, Malta, Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine.
About the role
A team ships a new service, and getting it from a laptop to production - built, tested, deployed, and visible when something goes wrong - shouldn't require that team to become infrastructure experts. It should be a paved road: a CI/CD pipeline that just works, observability that's there by default, and standard ways of doing things that are the obvious best choice, because the alternative is every team reinventing the same solutions on its own.
The Platform team builds and maintains that road, and the work is internal - the users are Cast AI's own engineers. The team is a multiplier for the engineering organization: whatever it builds is built once and serves every team, and the time product teams would otherwise spend on infrastructure goes back into developing their products.
What the team owns is whether the tools engineers use to build, deploy, and observe their services are good enough that reliability is actually achievable, and with hundreds of engineers and dozens of services leaning on that at once, the platform has to be fast, correct, and boring in the best sense.
Requirements:
Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices - not just make something work.
Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language.
Experience in platform engineering - you've built and operated (not just consumed) most, but not necessarily all of: CI/CD and GitOps systems (GitLab CI, Argo CD, Kargo), infrastructure as code (Terraform), container orchestration (Kubernetes, Helm), observability and monitoring tooling (VictoriaMetrics, Grafana, Loki, Tempo), and cloud platforms (AWS, GCP, or Azure).
Experience with AI coding agents and agentic development workflows.
You've personally driven a complex project end-to-end.
Strong debugging, optimization, and performance-tuning skills.
Strong English skills, both verbal and written.
Startup mindset: adaptable, proactive, and comfortable with ambiguity.
Participate in on-call rotation for teams that require it.
Nice to have:
Kubernetes operators, Helm chart authoring, admission webhooks.
Internal developer platform (IDP) design - golden paths, self-service tooling, service catalogs.
Local development environment tooling (devcontainers, Tilt, Skaffold, or similar).
Kubernetes or cloud-native OSS contributions.
Responsibilities:
Design, build, and operate the CI/CD pipelines every engineering team ships through.
Build and maintain the observability and monitoring tooling other teams use to debug their own systems in production.
Build local development environment tooling that lets engineers reproduce production-like conditions on their laptop.
Reduce friction for every other team: identify where engineers lose time to infrastructure and fix it at the root, not with a workaround.
Debug complex issues that span CI/CD, observability tooling, Kubernetes, and cloud infrastructure.
Contribute to the platform roadmap and backlog - shape what gets built next for the engineers who depend on you.
Tools we use daily:
Programming Language: Go
Cloud & Orchestration: Kubernetes, GCP, AWS, Azure
Infrastructure as Code: Terraform
Databases & Storage: ClickHouse, Cloud Object Storage
Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs