Today, Lonza is a global leader in life sciences operating across five continents. While we work in science, there’s no magic formula to how we do it. Our greatest scientific solution is talented people working together, devising ideas that help businesses to help people. In exchange, we let our people own their careers. Their ideas, big and small, genuinely improve the world. And that’s the kind of work we want to be part of.
The role:
We seek an adept expert to contribute significantly to our R&D team, bridging machine learning engineering with applied data science. You'll improve and manage our Machine Learning Operations (MLOps) on Azure, and participate in creating, assessing, and advancing various machine learning models and AI systems.
Collaborate extensively with scientific and operational teams to guarantee the robustness, scalability, and reliability of our AI tools. Implementing automation and standardization throughout the ML lifecycle, your efforts will support quicker, data-informed decision-making and boost innovation.
Help our CDMO's mission by turning research insights into practical solutions efficiently.
Key responsibilities:
Compose, construct, and uphold resilient machine learning operations (MLOps) pipelines that facilitate the complete lifecycle of AI models—from creation to implementation and supervision.
Guarantee the successful deployment of machine learning and large language models (LLMs) in practical operational settings, transforming research findings into functional business tools.
Facilitate the progress and examination of ML models, involving both standard machine learning and neural network-focused models, as requested by R&D teams.
Develop standardized, reusable workflows that can be applied across different projects and scientific areas.
Collaborate with scientists and engineers to incorporate AI solutions into daily R&D tasks.
Implement tools for version control, testing, and continuous integration to ensure quality, security, and traceability of AI solutions.
Develop automated reporting systems that make results from AI models easier to interpret, share, and act on.
Key requirements:
MSc or BSc in Computer Science, Data Science, Bioinformatics, Engineering, or a related field, or equivalent experience.
Proven experience designing and deploying MLOps pipelines (MLflow, Azure ML, Azure DevOps etc).
Strong programming skills in Python and familiarity with common ML/AI libraries (scikit-learn, tensorflow, Keras etc.).
Experience implementing machine learning and large language models (LLMs), encompassing deployment, monitoring, and retraining.
Familiarity with software engineering guidelines: version control (e.g., Git), CI/CD, containerization (e.g., Docker), and workflow orchestration.
Knowledge of cloud platforms and scalable compute environments (Azure preferred).
Understanding of data governance, model documentation, and reproducibility in a regulated or research-heavy context.
Ability to align machine learning initiatives with business objectives in a scientific or regulated environment.
Every day, Lonza’s products and services have a positive impact on millions of people. For us, this is not only a great privilege, but also a great responsibility. How we achieve our business results is just as important as the achievements themselves. At Lonza, we respect and protect our people and our environment. Any success we achieve is no success at all if not achieved ethically.
People come to Lonza for the challenge and creativity of solving complex problems and developing new ideas in life sciences. In return, we offer the satisfaction that comes with improving lives all around the world. The satisfaction that comes with making a meaningful difference.
Reference: R70331
