AI Developer / MLOps Engineer for Industrial AI, Edge AI & Databricks

Project overview

The focus is on automating ML retraining processes, further developing a Databricks-based training and monitoring platform, and optimizing industrial control loops through simulation. Another focus is on building an end-to-end observability solution for Edge AI services, from the edge system to the cloud.

A senior profile is sought, combining in-depth experience in Machine Learning, Data Engineering, MLOps, and Cloud/Edge architectures, capable of independently conceptualizing, implementing, and integrating technical solutions into existing support and operational processes.

Responsibilities

MLOps and automated model retraining

Simulation-based controller optimization

Databricks-based monitoring

Observability for Edge AI

Mandatory Requirements

Desirable Knowledge

Project Framework Data

If interested and professionally suitable, please send us your current profile including availability, workload, and remote hourly rate.

• Automation of the existing model retraining process
• Further development and operationalization of the training pipeline
• Orchestration and automation of ML pipelines
• Integration of the solution into existing support, operational, and deployment processes
• Tracking of model versions, experiments, and deployment statuses, for example with MLflow
• Development and validation of simulation models for industrial filling and packaging processes
• Automated optimization of controller parameters within the simulation
• Use of suitable machine learning and optimization methods
• Transfer of optimized parameters to a real test setup
• Validation and evaluation of results under practical conditions
• Optional: Use of Reinforcement Learning for optimizing industrial control processes
• Monitoring of data quality, data drift, model drift, and control loop performance
• Further development of existing monitoring dashboards
• Implementation of automated notifications for quality and performance deviations
• Development of suitable data quality checks, for example with DQX or comparable tools
• Analysis and preparation of large datasets with Spark and PySpark
• Collection, processing, and visualization of metrics, logs, and traces
• Development of real-time monitoring for Edge AI services
• Implementation of telemetry and observability with OpenTelemetry
• Development of an end-to-end monitoring and alerting concept from the edge system to the cloud
• Integration of the observability solution into existing cloud and operational environments
• Several years of practical experience in the development and operationalization of AI/ML solutions
• Very good knowledge of Python
• Experience with TensorFlow and/or PyTorch
• In-depth experience with Databricks or comparable Lakehouse platforms
• Very good knowledge of Spark and PySpark
• Experience with Data Lakes and Data Lakehouse architectures
• Experience in the design of ETL/ELT processes, data models, and data engineering architectures
• Experience with the orchestration and automation of ML pipelines
• Practical knowledge of MLflow or comparable MLOps tools
• Experience with Docker and Kubernetes
• Knowledge of AWS Cloud Services
• Experience with Edge and Cloud applications
• Very good knowledge of SQL and Git
• Experience with monitoring, alerting, and data quality concepts
• Experience with Infrastructure as Code, for example Terraform or AWS CloudFormation
• Very good German language skills for project communication
• Good English language skills for technical documentation
• Provision of services exclusively by resources within the EU or EEA
• Experience with OpenTelemetry
• Knowledge of Reinforcement Learning
• Experience in the simulation and optimization of industrial control processes
• Knowledge of control engineering, process automation, or industrial measurement technology
• Experience with production-related Edge AI architectures
• Knowledge of Model Drift Detection and Model Performance Monitoring
• Experience with DQX or comparable data quality tools
• Experience in mechanical engineering, plant engineering, production technology, or filling technology
• Confident and responsible use of AI-supported development tools
• Project start: 11/15/2026
• Project end: 03/31/2027
• Extension option: 04/01/2027 to 12/31/2027
• Utilization: 130 to 150 hours per month
• Deployment model: predominantly remote
• On-site component: approximately three days per quarter by individual agreement
• On-site deployment: at a customer site within Germany
• Project language: German
• Documentation: German and English
• Contract type: Freelance project collaboration
• Data protection requirement: Provision of services from the EU or EEA

Project details

• Start: 2026-11
• Remote: hybrid
• Location: München, Deutschland, DE
• Languages: German