Senior Software Engineer | AI Agents & Cloud Platform (Cast Pilot team)

About this 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

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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.

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Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language.

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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.

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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.

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You've personally driven a complex project end-to-end.

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Hands-on experience with AI coding agents and agentic development workflows. This is how we build, not just what we build.

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You've used observability tooling in production.

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CI/CD and DevOps practices experience.

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Strong English skills, both verbal and written.

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Startup mindset: adaptable, proactive, and comfortable with ambiguity.

What will make you stand out

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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.

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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.

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Experience with LLM application quality: evaluations, prompt engineering at scale, latency and cost optimization of agent loops.

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Knowledge of OAuth 2.0 / OIDC and multi-tenant authorization design.

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Deep knowledge of EKS, GKE, or AKS internals.

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Kubernetes or cloud-native OSS contributions.

Responsibilities

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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.

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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.

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Develop and maintain cloud connectivity across AWS, GCP, and Azure, and the data foundations our agents build on.

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Write production Go services that interact with cloud provider APIs, the Kubernetes control plane, and LLM APIs in real time.

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Own features end-to-end: from design through implementation, testing, and production rollout (most projects ship in 1 to 4 weeks).

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Debug novel problems: an agent giving a wrong answer, a cross-cloud permission edge case, a data freshness issue that only appears at scale.

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Build the quality harness: evals, telemetry, feedback loops, that let us ship agent improvements without regressions.

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Contribute to product direction and backlog. Shape what gets built next, not just how it's built.

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Participate in the team's on-call rotation.

Tools We Use Daily

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Programming Languages: Go

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AI & Agents: MCP (servers and clients), agent gateways, LLM provider APIs, RAG, evaluation pipelines

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Cloud & Orchestration: Kubernetes, AWS, GCP, Azure

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Infrastructure as Code: Terraform

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Databases & Storage: PostgreSQL, Cloud Object Storage, ClickHouse

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Workflows & APIs: Temporal, GCP Pub/Sub, gRPC, REST

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Observability & Product Analytics: Prometheus, Grafana, Loki, Tempo, PostHog

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CI/CD & GitOps: GitLab/GitHub CI with ArgoCD and Kargo

What's in it for you?

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Competitive salary (€6,500 to €9,000 gross, depending on the level of experience)

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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.

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Equity…