MLOps (Machine Learning Operations) Engineer Join to apply for the
MLOps (Machine Learning Operations) Engineer
role at
Boehringer Ingelheim.
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.
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 adecuado 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
Benefits
Flexible working conditions
Life and accident insurance
Health insurance at a competitive price
Investment in learning and development
Gym membership discounts
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