About the role
What you'll be solving
A platform engineer opens their AI client of choice, connects it to CAST AI, and types: "Why did our spend jump 40% in eu-west last week?" Thirty seconds later they have an answer, pulled from live cluster state and cost data, with links back to the evidence. No dashboards. No YAML archaeology. Just a question and a correct, permission-checked answer.
Your job is to make that interaction boring. Reliably, securely, at production scale.
Behind it sits the hard part: an AI assistant that reasons over thousands of production Kubernetes clusters, a platform that safely exposes company infrastructure to external AI clients, and quality pipelines that tell us when the agent is wrong before a customer does. Get it right, and operators stop clicking through consoles entirely. Get it wrong once, and an agent gives a confidently wrong answer to someone managing production.
CAST AI already runs Kubernetes infrastructure autonomously. Our automation makes thousands of cost-and-reliability decisions a day. Cast Pilot is the team building the intelligence layer on top of that: AI assistants for Kubernetes and the cloud itself. Our assistant already answers thousands of questions a month from hundreds of organizations. Now we're opening the platform up: a public, permission-aware MCP server so customers can plug CAST AI straight into their own AI clients and agents, deeper connectivity across all three major clouds (AWS, GCP, Azure), and a continuous push to make our agents smarter, better grounded, and more capable.
This is greenfield AI platform work on top of battle-tested infrastructure. Most features ship in 1 to 4 weeks.
This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Cyprus, Malta, Finland, Sweden, Ireland, Moldova, North Macedonia, Montenegro, Slovenia, and Ukraine.
Requirements • Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices, not just make something work. You're a generalist who is as comfortable debugging a distributed system as designing an auth flow or tuning an agent's behavior. • Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. • Experience building or operating large-scale SaaS platforms in production: multi-tenant systems, high volumes, and the failure modes that only show up at scale. • Solid understanding of Kubernetes. Our entire platform runs on it, and the assistants we build answer questions about it. You should be at home with autoscaling, scheduling, and networking concepts. • You've personally driven a complex project end-to-end. • Hands-on experience with AI coding agents and agentic development workflows. This is how we build, not just what we build. • You've used observability tooling in production. • CI/CD and DevOps practices experience. • Strong English skills, both verbal and written. • Startup mindset: adaptable, proactive, and comfortable with ambiguity.
What will make you stand out • You've built agentic systems that shipped: MCP servers or clients, agent gateways, tool-use orchestration, RAG pipelines, evaluation frameworks, and you can talk about what broke in production and how you found out. This is the single biggest differentiator for our team. • Deep hands-on experience with one or more of AWS, GCP, or Azure, with real understanding of IAM, compute, networking, and their APIs, not just console-level usage. • Experience with LLM application quality: evaluations, prompt engineering at scale, latency and cost optimization of agent loops. • Knowledge of OAuth 2.0 / OIDC and multi-tenant authorization design. • Deep knowledge of EKS, GKE, or AKS internals. • Kubernetes or cloud-native OSS contributions.
Responsibilities • Design and build the systems behind our AI assistant: agent flows, context retrieval, and the data connections that let it reason about live Kubernetes infrastructure. • Build and evolve our public, permission-aware MCP server: curated tools, identity, audit, and strict safety gates, so customers can use CAST AI from their own AI clients. • Develop and maintain cloud connectivity across AWS, GCP, and Azure, and the data foundations our agents build on. • Write production Go services that interact with cloud provider APIs, the Kubernetes control plane, and LLM APIs in real time. • Own features end-to-end: from design through implementation, testing, and production rollout (most projects ship in 1 to 4 weeks). • Debug novel problems: an agent giving a wrong answer, a cross-cloud permission edge case, a data freshness issue that only appears at scale. • Build the quality harness: evals, telemetry, feedback loops, that let us ship agent improvements without regressions. • Contribute to product direction and backlog. Shape what gets built next, not just how it's built. • Participate in the team's on-call rotation.
Tools We Use Daily • Programming Languages: Go • AI & Agents: MCP (servers and clients), agent gateways, LLM provider APIs, RAG, evaluation pipelines • Cloud & Orchestration: Kubernetes, AWS, GCP, Azure • Infrastructure as Code: Terraform • Databases & Storage: PostgreSQL, Cloud Object Storage, ClickHouse • Workflows & APIs: Temporal, GCP Pub/Sub, gRPC, REST • Observability & Product Analytics: Prometheus, Grafana, Loki, Tempo, PostHog • CI/CD & GitOps: GitLab/GitHub CI with ArgoCD and Kargo
What's in it for you? • Competitive salary (€6,500 to €9,000 gross, depending on the level of experience) • Work on genuinely new problems: production MCP platforms and AI assistants for Kubernetes and cloud infrastructure are not solved domains. You'll be defining them, not following a playbook. • Equity options. • Real users, fast: your work ships to hundreds of organizations in weeks, not quarters. • Enjoy a flexible, remote-first global environment. • Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. • Spend 10% of your work time on personal projects or self-improvement. • Learning budget for professional and personal development, including access to international conferences and courses. • Annual hackathon to spark new ideas and strengthen team bonds. • Team-building budget and company events to connect with your colleagues. • Equipment budget to ensure you have everything you need. • Extra days off to help maintain a healthy work-life balance.
Hiring process • Screening call with Recruiter • Hiring Manager interview • Technical interview (system design) • Live coding • Culture Check interview with an executive
As part of our standard hiring process, 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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