Machine Learning Engineer

RockstarDevelopers GmbH

Machine Learning Engineer building productive AI solutions for public sector clients. Collaborating in a remote-first environment within the DACH region.

Posted 7/23/2026full-timeRemote • 🇩🇪 GermanyJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying LLM applications and dialogue-based systems, with a strong focus on MLOps practices and privacy-compliant AI design. Proficient in backend development with Python and experienced in agile methodologies, ensuring reliable and scalable machine learning solutions.

Highest-signal resume keywords

Machine Learning EngineeringBackend Development with PythonLLM Orchestration with LangChainMLOps in ProductionGerman Language Proficiency (C1)

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Hard Skills

Machine Learning SolutionsDialogue-Based SystemsSemantic SearchKnowledge-Base ManagementVector SearchSemantic IndexingData Pipeline DevelopmentError AnalysisPrivacy-Compliant AI DesignAgile Development

Tools & Technologies

FastAPIGrafanaKubernetesArgoCDJenkins

Industry Keywords

Production-Grade AI SystemsMulti-Tenant EnvironmentPublic Sector ProjectsAgile Delivery FrameworksObservability

Tech Stack

Tools & technologies

GrafanaJenkinsKubernetesPython

About the role

Key responsibilities & impact

Build LLM applications.
Develop dialogue-based systems and semantic search and integrate them into the domain-specific applications of the industry solution.
Set up and maintain RAG systems, including knowledge-base management: indexing, updates, and clean source-separated storage of structured and unstructured content.
Operation and monitoring.
MLOps in production: observability, structured logging, and error analysis.
Ensure systems run reliably, not just that they worked once.
Deploy agents to production, from development and integration through to stable delivery.
Improve systems based on monitoring data, testing, and user feedback.
Identify new AI use cases, prototype them, and build data pipelines from preprocessing and model development to production.

Requirements

What you’ll need

At least 2 years of professional experience as a Machine Learning Engineer in designing, developing, implementing, and optimizing scalable ML solutions.
At least 2 years of experience working in agile development teams.
German at least C1 level (spoken and written), demonstrable via a language certificate or as a native speaker.
Degree in Computer Science, Business Informatics, or a comparable qualification, verifiable by diploma or self-declaration.
Ideally: vector search and semantic indexing in vector databases, preferably Milvus.
Backend development with Python, preferably FastAPI.
LLM orchestration with LangChain or LangGraph.
Operating production-grade AI systems: monitoring (ideally Grafana), structured logging, error analysis, deployment.
Privacy-compliant AI design, especially when handling personal data in logging and observability.
Experience deploying GenAI for many users, preferably in a multi-tenant environment.
Kubernetes, ArgoCD, Jenkins.
Experience with agile delivery frameworks, ideally SAFe.
Experience from public sector projects.

Benefits

Comp & perks

Real production projects.
AI that goes live with clients.
Not an innovation lab — no endless slide decks.
Remote-first within the DACH region.
Occasionally on-site; otherwise work from wherever you are most productive.
Modern AI stack.
RAG, agents, vector search, MLOps.
Current stack, no legacy baggage.
Internal upskilling.
We invest in your AI skills.
MacBook provided, unless the client supplies their own hardware.
Flat hierarchies.
The founders are your direct contacts.
A team that knows each other, even when working distributed.