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Qualifikationen
5+ years of professional software engineering experience delivering features and infrastructure in production.
Hands-on experience building and maintaining CI/CD systems at org scale (GitLab CI and/or Jenkins).
Experience building developer-facing tooling or platform services used by other engineers.
Hands-on experience with LLM developer tooling (MCP, LLM APIs, agent orchestration, or AI harnesses).
Deep proficiency in Python or TypeScript with production ownership.
Proficiency with Kubernetes and Helm at production scale on AWS or Azure.
Experience designing shared pipeline abstractions and CI/CD infrastructure used by multiple teams.
Familiarity with infrastructure-as-code tools (Terraform, Pulumi).
Proficiency with Git, Docker, automated testing, and modern scripting languages.
Active daily use of AI-assisted development tools.
Bachelor's degree in Computer Science, Software Engineering, or equivalent.
Aufgaben
Owns features and infrastructure end-to-end: design through production release.
Identifies edge cases and failure modes independently within assigned scope.
Participates actively in code review with constructive, specific feedback.
Surfaces blockers early rather than waiting for check-ins.
Designs pipeline abstractions that work across multiple teams and tech stacks.
Keeps pipelines healthy, observable, and continuously improving.
Owns the shared infrastructure layer for autonomous AI agent environments: orchestration, provisioning, observability, cost controls, and security guardrails.
Treats engineers as customers: office hours, documentation, feedback loops.
Measures platform impact with DORA metrics, adoption rates, and time-to-productivity data.
Kenntnisse
Python
TypeScript
Kubernetes
CI/CD
Terraform
Pulumi
LLM tooling
AWS
Azure
Docker
Git
Observability
AI tooling
Ausbildung
Bachelor's degree in Computer Science or related field
Tools
GitLab CI
Jenkins
Kubernetes
Helm
Jobbeschreibung
Responsibilities
Owns features and infrastructure end-to-end: design through production release, limited guidance required
Identifies edge cases and failure modes independently within assigned scope
Participates actively in code review with constructive, specific feedback
Surfaces blockers early rather than waiting for check-ins
Designs pipeline abstractions (templates, shared jobs, reusable configs) that work across multiple teams and tech stacks
Keeps pipelines healthy, observable, and continuously improving
Owns the shared infrastructure layer for autonomous AI agent environments: orchestration, provisioning, observability, cost controls, and security guardrails
Treats engineers as customers: office hours, documentation, feedback loops
Measures platform impact with DORA metrics, adoption rates, and time‑to‑productivity data
Requirements
5+ years of professional software engineering experience, delivering features and infrastructure independently in production
Hands‑on experience building and maintaining CI/CD systems at org scale, preferably GitLab CI and/or Jenkins
Experience building developer‑facing tooling or platform services other engineers depend on
Hands‑on experience with LLM developer tooling: MCP, LLM APIs, agent orchestration, or AI harnesses (Claude Code, Cursor, Copilot Workspace, or equivalent)
Deep proficiency in Python or TypeScript, with production experience sufficient to own and deliver real features
Proficiency with Kubernetes and Helm at production scale on AWS or Azure
Experience designing shared pipeline abstractions and CI/CD infrastructure used by multiple teams
Familiarity with infrastructure‑as‑code tools (Terraform, Pulumi, or equivalent)
Proficiency with standard development tooling: Git, Docker, automated testing, and modern scripting languages
Active daily use of AI‑assisted development tools
Bachelor's degree in Computer Science, Software Engineering, or equivalent experience.
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