Required skills
- Databricks
- Data Lake
- Data Lakehouse
- Spark
- PySpark
- Data Engineering
- Python
- SQL
- Data Modeling
- ETL
- ELT
- MLOps
- ML Pipelines
- Orchestration
- Automation
- MLflow
- TensorFlow
- PyTorch
- AWS
- Docker
- Kubernetes
- Terraform
- CloudFormation
- Monitoring
- Observability
- Data Quality
- OpenTelemetry
- DQX
- Edge AI
- Cloud Applications
Languages: German
Job description
Seeking an experienced MLOps & Data Engineer to automate ML processes, optimize controller parameters using simulations, and implement monitoring solutions for industrial plant and filling environments.
Project overview
For a project in the industrial plant and filling environment, we are looking for an experienced "MLOps & Data Engineer" to drive the automation of ML processes, simulation-based controller optimization, and monitoring and observability solutions for edge and cloud applications.
Responsibilities
The engagement involves a workload of 130-150 hours per month. 3 onsite days per quarter are planned.
- Automation of model retraining as well as further development and orchestration of existing ML and training pipelines
- Development and validation of simulation models for filling processes as well as automated optimization of controller parameters and transfer to the real test setup
- Setup and further development of Databricks-based monitoring for data quality, data drift, and control loop performance
- Conception and implementation of observability, real-time monitoring, and alerting for edge AI services from the edge to the cloud
- Integration of the developed solutions and monitoring processes into existing support processes
Requirements
- Experience with Databricks or comparable platforms, Data Lakes/Lakehouses, and Spark/PySpark
- Profound knowledge in Data Engineering with Python and SQL, especially data modeling and ETL/ELT design and architecture
- Experience with MLOps and ML pipelines, including orchestration, automation, MLflow, and TensorFlow or PyTorch
- Knowledge in cloud, container, and infrastructure-as-code environments, especially AWS, Docker, Kubernetes, and Terraform/CloudFormation
- Experience with monitoring, observability, and data quality, e.g., OpenTelemetry and DQX, as well as edge and cloud applications
Nice to have
Environment:
Databricks, Data Lake, Data Lakehouse, AWS Cloud Services, Spark, PySpark, Python, SQL, MLflow, TensorFlow, PyTorch, Docker, Kubernetes, Terraform, CloudFormation, OpenTelemetry, DQX, Git, Reinforcement Learning, Edge AI, AI-assisted programming
- Knowledge in Reinforcement Learning
- Experience with AI-assisted programming
Project details
- Start: 16.11.2026
- Duration: 4 Monate++
- Location: Hybrid, DE
- Languages: German
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