Senior Software Engineer - AI Agents & Cloud Platform

castaigroupinc; Spain; On-site; EUR 90,000 - 130,000; Full time

Job summary

CAST AI is seeking a platform engineer to design and build the systems behind our AI assistant that reasons over thousands of production Kubernetes clusters. You will work on agent flows, context retrieval, and the data connections that let it reason about live infrastructure.

You’ll develop and maintain a public, permission-aware MCP server, with curated tools, identity, audit, and strict safety gates so customers can use CAST AI via their own AI clients.

Qualifications

• Strong software engineering fundamentals with clean abstractions and maintainable code.

Responsibilities

• Design and build the systems behind our AI assistant, including agent flows and context retrieval.
• Build and evolve our public, permission-aware MCP server with curated tools, identity, audit, and safety gates.
• Develop and maintain cloud connectivity across AWS, GCP, and Azure, and support data foundations for agents.
• Write production Go services that interact with cloud provider APIs, Kubernetes, and LLM APIs in real time.
• Own features end-to-end from design through production rollout (typically 1–4 weeks).
• Debug novel problems such as wrong answers or cross-cloud permission edge cases.

Skills

Go programming

Distributed systems

Kubernetes

Auth flow design

CI/CD

Observability

English proficiency

Startup mindset

Multi-tenant SaaS

Agentic development workflows

Tools

AWS

GCP

Azure

OAuth 2.0 / OIDC

EKS/GKE/AKS

Job description

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.

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