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., * 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

Artificial Intelligence (AI) Agents, Software Engineering, Cloud Computing, Artificial Intelligence (AI), Boring, Production Management, Automation, MCP - Microsoft Certified Professional, Team Building, Cloud Computing, * 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.

• 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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