Job Title : MLOps Engineer Location : Antwerp, Belgium Languages : English Project Objectives The candidate will play a pivotal role in leveraging data science and machine learning tools to enhance drug formulation and process development within the Global Pharmaceutical Product Development and Supply (PPDS) organization. The project focuses on: Data Preparation : Analyze existing data sources and build robust data pipelines using Python for ingestion into a temporary database. Utilize this database to test and evaluate initial analyses and candidate machine learning (ML) models. ML Model Investigation : Collaborate with chemical process engineers and scientists to research different types of ML models. Formulate recommendations on the most suitable ML model or determine additional data requirements. Solution Deployment : Transition the solution to the Snowflake/DBT reference architecture, ensuring early compatibility with the architecture. About the Department The PPDS organization is part of the Therapeutics Development and Supply (TDS) sector of Client and focuses on the development of pharmaceutical dosage forms. The team aims to: Strengthen process formulation capabilities for oral and parenteral solid and liquid dosage forms. Define and develop predictive process models to accelerate the scale-up of drug products between labs and production plants. Leverage data sciences and process modeling tools to fully understand process behavior and deliver the best product and process at launch. Key Responsibilities Data Engineering : Design and develop efficient data pipelines and databases for large-scale data processing. Automate ETL processes for data extraction, transformation, and loading. Collaborative Development : Work with cross-functional teams to define data requirements and ensure integration of data systems. Data Visualization : Create user-friendly front-end applications for data visualizations and automatic reporting. Machine Learning : Develop, implement, and manage machine learning models to improve process efficiency and quality. Follow best practices for model versioning, monitoring, and tracking to ensure regulatory compliance. Toolbox Development : Enhance the process modeling toolbox using AI/ML techniques and identify ongoing opportunities for optimization. Knowledge Sharing : Share expertise with colleagues and communicate results and innovative ideas clearly to stakeholders. Qualifications and Requirements Education : PhD or Master’s in Data Sciences, Computer Sciences, Engineering, or a related field with 4 years’ experience. Experience : Proven experience in MLOps or similar roles within pharmaceutical, biotechnology, or healthcare sectors. Strong programming skills in Python, R, or Matlab, particularly in data manipulation and working with ML frameworks (e.g., Tensorflow, PyTorch, Scikit-learn). Familiarity with cloud platforms (e.g., AWS, Azure) for model deployment. Solid understanding of machine learning algorithms and model evaluation in the context of process development. Soft Skills : Excellent problem-solving skills and the ability to work collaboratively in cross-functional teams. Effective communication of complex concepts to non-technical stakeholders. Strong oral and written communication skills in English .