Machine Learning Ops Engineer

Beschreibung

Job Zusammenfassung In dieser Rolle gestaltest du ML-Pipelines für Training, Validierung und Deployment, operationalisierst ML-Modelle, verbesserst die Entwicklungsumgebung für ML-Ingenieure und sorgst für reibungslose Datenflüsse in der Produktion. Job Zusammenfassung In dieser Rolle gestaltest du ML-Pipelines für Training, Validierung und Deployment, operationalisierst ML-Modelle, verbesserst die Entwicklungsumgebung für ML-Ingenieure und sorgst für reibungslose Datenflüsse in der Produktion. Deine Rolle im Team Design, build, and maintain ML pipelines covering training, validation, deployment, monitoring, and retraining. Operationalize ML models developed in collaboration with ML, data and backend engineers, and ensure their reliability in production. Support experimentation by making ML systems easier to deploy, monitor, and iterate on. Build up the developer experience for ML engineers (environment setup, dependency management, automation, CI/CD). Focused on reusability, standardization and scalable development workflows. Apply strong software engineering practices within ML codebases (modularity, testing, version control, code reviews). Contribute to modeling tasks when needed, including data preparation, feature engineering, experiment execution, and evaluation. Collaborate closely with data engineers and backend engineers to ensure clean data flows and robust integrations. Unsere Erwartungen an dich Ausbildung Degree in Computer Science, Software Engineering, Data Engineering, or related technical disciplines. Qualifikationen Strong programming skills, especially in Python and SQL, with a clear software engineering mindset. Erfahrung 5 years of professional experience in software engineering, ML engineering, or data-intensive engineering roles. Hands-on experience building, shipping and maintaining production ML systems, pipelines, or data workflows. Experience with cloud-based environments and production infrastructure. Experience working with large-scale datasets and distributed processing frameworks (e.g. Spark or similar). Practical experience of the ML lifecycle and ability to collaborate effectively on modeling tasks. Experience contributing to collaborative codebases using Git and following structured development practices (pull requests, reviews, branching workflows). Unser Angebot Competitive salary and comprehensive benefits package. Autonomy to explore and implement new technologies, tools, and partners. Work in a dynamic environment with high exposure to a wide variety of genres, tools, and diversified products. Flexible working hours and a supportive, collaborative work environment. Opportunity to work with a talented team of professionals and make a significant impact on a globally recognized product. Benefits Gesundheit, Fitness & Fun 🎳 Team Events Themen mit denen du dich im Job beschäftigst Machine Learning AI Gaming Industry Metadaten Level: Senior Job Feld: Software, Data Anstellung: Vollzeit Vertragsart: Unbefristetes Dienstverhältnis Arbeitsmodell: Hybrid, Onsite Unternehmenstyp: Etablierte Firma Branche: Internet, IT, Telekom Ort: Hamburg | Deutschland

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