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Machine Learning Engineer – m/f/d
RockstarDevelopers GmbHMachine Learning Engineer building productive AI applications for large public sector clients. Remote-first within the DACH region, integrating solutions into industry-specific applications.
Core Competencies
Role fitCore Competencies
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Demonstrates expertise in building LLM-based applications and developing conversational systems, with a strong focus on MLOps practices and production-grade AI system operation. Proficient in backend development with Python and experienced in agile methodologies, ensuring reliable and privacy-compliant AI solutions.
Highest-signal resume keywords
Machine Learning EngineeringLLM Orchestration with LangChainBackend Development with PythonVector Search and Semantic IndexingMLOps in Production
ATS Keywords
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Hard Skills
Machine Learning EngineeringLLM OrchestrationBackend DevelopmentVector SearchSemantic IndexingMLOpsPrivacy-Compliant AI DesignData Pipeline DevelopmentAgile DevelopmentError Analysis
Soft Skills
CollaborationProblem-SolvingIterative ImprovementUser Feedback IntegrationCommunication
Tools & Technologies
PythonFastAPIGrafanaKubernetesArgoCDJenkinsMilvusLangChainLangGraphRAG Systems
Certifications & Qualifications
German Language Proficiency (C1)
Industry Keywords
AI SystemsPublic Administration SectorAgile Delivery StructuresMulti-Tenant EnvironmentObservability
Tech Stack
Tools & technologiesGrafanaJenkinsKubernetesPython
About the role
Key responsibilities & impact- Build LLM-based applications.
- Develop conversational systems and semantic search and integrate them into the domain-specific applications of the industry solution.
- Set up and maintain RAG (Retrieval-Augmented Generation) 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 in production, not just that they worked once.
- Deploy agents into production.
- From development through integration and connection to portal systems to stable delivery.
- Improve solutions based on data.
- Use monitoring data, tests, and user feedback to iteratively improve solutions.
- Experiment with new approaches.
- 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 3 years of professional experience as a Machine Learning Engineer in the design, development, implementation, and optimization of scalable ML solutions.
- At least 2 years of experience working in agile development teams.
- German language proficiency at least at C1 level (spoken and written), demonstrable by a language certificate or as a native speaker.
- Completed degree in Computer Science, Business Informatics, or a comparable qualification, verifiable by certificate or self-declaration.
- Vector search and semantic indexing in vector databases, ideally Milvus.
- Backend development with Python, preferably FastAPI.
- LLM orchestration with LangChain or LangGraph.
- Operation of 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.
- GenAI in use for many users, ideally in a multi-tenant environment.
- Kubernetes, ArgoCD, Jenkins.
- Experience with agile delivery structures, ideally SAFe.
- Experience from projects in the public administration sector.
Benefits
Comp & perks- Real production projects.
- AI that goes into operation at customer sites.
- Not an innovation lab — real deployments, not just slide decks.
- Remote-first within the DACH region.
- Occasional on-site presence; 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.
- Founders are your direct contacts.
- A team that knows each other, even when working remotely.