MLOps (Machine Learning Operations) Engineer

Build the Bridge Between Data Science Innovation and Production Environments

Join our Data Science and Insights Team as an MLOps Engineer who will help transform experimental models into reliable, production-ready infrastructure solutions. You'll be a key contributor linking data science experimentation with operational deployment, ensuring ML solutions deliver sustained value across IT Infrastructure.
You'll contribute to establishing the MLOps foundation that enables our Data Scientists and Analysts to move from proof-of-concept to production, while supporting the data pipelines that fuel our analytics and ML initiatives. Your work directly impacts infrastructure reliability, automation, and the evolution toward autonomous operations. This is an internal permanent position with excellent opportunities for professional growth in a rapidly evolving field.

Responsibilities

• Support the operationalization of ML models by helping transform data science experiments into production-ready systems with monitoring, versioning, and automated workflows.
• Contribute to designing and implementing CI/CD pipelines for ML model deployment, ensuring reproducibility and reliability across development and production environments.
• Assist in creating frameworks for ML lifecycle management including experiment tracking, model registry, performance monitoring, and model updates.
• Design and implement data pipelines (ETL/ELT) that support both ML model training and analytics use cases with proper data quality validation.
  Implement data validation frameworks, quality checks, and monitoring systems to ensure reliable data flows for machine learning and analytics applications.
• Collaborate with Data Scientists and Analytics to understand model requirements and contribute to scalable deployment solutions and infrastructure optimization.
• Work with the Data Engineering team and Analytics Engineers to leverage existing data architecture and integration patterns.
• Build reusable data transformation components that enable Data Scientists and Analysts to access clean, structured data efficiently.
• Create technical documentation for MLOps processes, deployment procedures, and data pipelines.
• Help identify and resolve bottlenecks in ML and data pipelines while contributing to cost-effective infrastructure utilization.

Requirements

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related quantitative fields.
• 1-3 years of experience in MLOps, ML Engineering, DevOps, or Software Engineering roles with exposure to ML model deployment or data pipeline development.
• Strong Python programming skills with hands-on experience in ML frameworks (scikit-learn, TensorFlow, PyTorch) or similar data science tools.
• Understanding of ML workflows from model training through deployment, demonstrated through professional work, academic projects, or personal initiatives.
• Basic experience with data pipelines and data processing, with demonstrated interest in building scalable data systems.
• Familiarity with containerization and basic understanding of infrastructure-as-code concepts.
• Working knowledge of SQL with basic understanding of relational databases and data modeling principles.
• Familiarity with Pandas for data transformation and interest in working with complex datasets.
• Basic understanding of cloud platforms (AWS) with willingness to learn cloud data platforms and ML services.
• Solution-oriented mindset with ability to work pragmatically within organizational constraints and deliver incrementally.
• Fluent in English (written and oral) with ability to document technical designs and collaborate with cross-functional teams.

Personal Attributes

• Pragmatic problem-solving approach with focus on practical solutions that balance ideal architectures with real-world constraints.
• Strong learning agility with demonstrated ability to quickly adopt new technologies and adapt to evolving requirements.
• Excellent collaboration skills with ability to work effectively across Data Science, Analytics, Data Engineering, and IT Infrastructure teams.
• Clear communicator with good documentation habits and commitment to knowledge sharing within the team.
• Quality-focused professional with commitment to reliability and maintainability without overengineering solutions.
• Team player who thrives in a growing team environment where processes and infrastructure are being established collaboratively.
• Self-motivated individual comfortable with ambiguity and eager to contribute to building something meaningful from the ground up.

#IamBoehringerIngelheim because…

We are continuously working to design the best experience for you. Here are some examples of how we will take care of you:

• Flexible working conditions
• Life and accident insurance
• Health insurance at a competitive price
• Investment in your learning and development
• Gym membership discounts

If you have read this far, what are you waiting for to apply? We want to know more about you!