Overview

Data-informed decision-making and boost innovation. Help our CDMO's mission by turning research insights into practical solutions efficiently. This role focuses on building and deploying MLOps pipelines, deploying ML and LLM capabilities, and collaborating with R&D teams to integrate AI into daily workflows.

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.

About Lonza

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. At Lonza, we respect and protect our people and our environment. Any success we achieve is no success at all if not achieved ethically. We offer the satisfaction that comes with improving lives around the world.