Stellenangebote für: - SENIOR ML OPS ENGINEER

Salary: 65.000 - 100.000 per year Requirements: Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines) Proficiency in building ML-Pipelines for productive use IaC (Infrastructure as Code) coding ...

Salary: 65.000 - 100.000 per year Requirements: Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines) Proficiency in building ML-Pipelines for productive use IaC (Infrastructure as Code) coding ...

Salary: 65.000 - 100.000 per year Requirements: Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines) Proficiency in building ML-Pipelines for productive use IaC (Infrastructure as Code) coding ...

Salary: 65.000 - 100.000 per year Requirements: Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines) Proficiency in building ML-Pipelines for productive use IaC (Infrastructure as Code) coding ...

Salary: 65.000 - 100.000 per year Requirements: Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines) Proficiency in building ML-Pipelines for productive use IaC (Infrastructure as Code) coding ...

Salary: 65.000 - 100.000 per year Requirements: Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines) Proficiency in building ML-Pipelines for productive use IaC (Infrastructure as Code) coding ...

Salary: 65.000 - 100.000 per year Requirements: Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines) Proficiency in building ML-Pipelines for productive use IaC (Infrastructure as Code) coding ...

Salary: 65.000 - 100.000 per year Requirements: Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines) Proficiency in building ML-Pipelines for productive use IaC (Infrastructure as Code) coding ...

Salary: 65.000 - 100.000 per year Requirements: Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines) Proficiency in building ML-Pipelines for productive use IaC (Infrastructure as Code) coding ...

Position Description As a (Senior) ML-Ops Engineer, you will play a crucial role in building and maintaining the infrastructure and processes required to support machine learning operations. You will be responsible for curating datasets, evaluating machine learning models using key performance indicators (KPIs), validating, deploying and monitoring models, and ensuring their seamless integration into production systems (embedded and Cloud). Additionally, you will design and implement CI/CD pipel...

Stellen-ID: 61991 ab sofort und in Vollzeit an unserem BWI Standort in Berlin, alternativ in München. Stellenbeschreibung Sorgen Sie gemeinsam mit uns für die digitale Zukunftsfähigkeit der Bundeswehr. Als primärer Digitalisierungspartner der Bundeswehr erbringen wir stabile, sichere und effiziente IT-Services im In- und Ausland, vom Grundbetrieb bis in den einsatznahen Bereich und tragen so zur kontinuierlichen Erhöhung der Führungs- und Einsatzfähigkeit der Bundeswehr bei. Mit über 7.700 Kolle...

Einleitung Dies ist keine klassische ML- oder Data-Science-Rolle. Du trainierst keine Modelle, sondern: automatisierst Geschäftsprozesse mit n8n und KI-APIs (OpenAI, Anthropic u. a.), ggf. unter Nutzung von Finetuning baust schlanke Full-Stack-Services (z. B. Next.js + Postgres oder FastAPI + SQLite) denkst "Customer First" und lieferst praktikable Lösungen in Tagen, nicht in Monaten. Warum jetzt? Generative KI entfaltet gerade erst ihr Potenzial - Automatisierung macht sie skalierbar. Bei Lucid...