We are seeking an MLOps Engineer to operationalize ML models and build scalable pipelines for deployment in a variety of hybrid cloud/on-prem environments, including air-gapped and power-constrained systems. This role focuses on bridging predevelopment and product readiness while ensuring robustness, efficiency, and compatibility.
Your tasks
* Design and implement AI/ML pipelines (data ingestion, model training, deployment) for hybrid cloud/on-prem and edge environments.
* Containerise (e.g., Docker, Podman) AI applications in close contact with our AI/ML engineers.
* Deploy models to cloud and on-prem infrastructure (e.g., Kubernetes, Azure).
* Optimize our AI models for air-gapped and low-power systems.
* Ensure a secure model deployment adhering to MLOps best practices (e.g., ISO 42001)
* Optimize model performance (latency, accuracy) and monitor in production (logging, A/B testing) for edge and hybrid deployments.
* Collaborate with the pre-development team to refine prototypes for edge AI and constrained environments.
* Ensure reproducibility and scalability using MLOps tools (e.g., MLflow) and edge-specific frameworks.
Your qualifications
* BS / MSc in Computer Science, Software Engineering, Electrical Engineering or a related field.
* 3+ years of hands-on MLOps engineering experience in product ready environments.
* Proven experience with hybrid cloud/on-prem deployment strategies (e.g., Azure, Kubernetes, Docker).
* Experience deploying models in air-gapped and power-constrained environments (e.g., edge devices, embedded systems).
* Proficiency in Python, cloud platforms, and containerization (Docker, Kubernetes, GitOps).
* Strong knowledge of ML pipelines and CI/CD workflows.
* Familiarity with edge AI tools (e.g., TensorFlow Lite, ONNX).
* DevOps mindset with a focus on automation and reliability.
* Ability to troubleshoot both model and infrastructure issues.
* Fluent English required; German language skills are a plus.
Interested?
We are looking forward to receiving your application! Ideally, you should apply online with the reference number. If you have any questions, please feel free to contact your recruiting contact via LinkedIn or XING.
Equal opportunities are important to us. We are looking forward to receiving your application regardless of gender, nationality, ethnic and social origin, religion, ideology, disability, age as well as sexual orientation and identity.
Reference number: 15304
Recruiting contact: Jun-Ho Song