Madrid Data Engineer M 28050

Job description

DATA Engineer 10/5/26 General Responsibility We are seeking a highly skilled Data Engineer / Machine Learning Engineer to join our Applied AI Team. The ideal candidate combines strong software engineering foundations with hands-on experience in data pipelines and machine learning systems, and enjoys working at the intersection between data, models, and production systems. As a Data Engineer / MLE at AI Labs, you will work closely with data scientists, software engineers, and product owners to design, build, deploy, and operate end-to-end data and machine learning solutions across multiple business units — including Regulatory, Clinical Trials, R&D, Pharmacovigilance, and Drug Manufacturing. This role is critical to ensuring that AI models move reliably from experimentation to production, supported by scalable data pipelines, robust ML infrastructure, and strong engineering standards. Specific Responsibilities Design, build, and maintain scalable data pipelines for data ingestion, transformation, and serving, supporting both analytics and machine learning use cases. Develop and productionize machine learning pipelines, covering training, validation, deployment, and monitoring. Collaborate closely with Data Scientists to translate notebooks and prototypes into robust, production-ready ML systems. Implement model deployment patterns (batch, real-time, or hybrid) using APIs, scheduled jobs, or event-driven architectures. Build and maintain feature pipelines and data abstractions that enable reproducible and reliable model behavior. Ensure data quality, versioning, and traceability across datasets and models. Optimize pipelines and ML workloads for performance, scalability, and cost efficiency. Work with DevOps and Platform teams to deploy solutions using containerization and CI/CD best practices. Contribute to defining data engineering and MLOps standards across AI Labs. Participate in code reviews, documentation, and mentoring to foster a culture of engineering excellence. Requirements and personal skills Proficient in Spanish and English, written and verbal communication. Strong proficiency in Python, including clean code practices, packaging, and modular design. Solid understanding of software engineering principles (OOP, SOLID, testing, version control). Hands-on experience building data pipelines (ETL / ELT) using Python-based frameworks or custom solutions. Experience working with machine learning workflows, including model training, evaluation, and deployment. Familiarity with REST APIs and service-based architectures (FastAPI, Flask, or similar). Strong experience with Git and collaborative development workflows. Experience with containerization (Docker) and cloud environments (AWS or Azure). Experience with MLOps practices (model versioning, monitoring, drift detection, retraining strategies). Familiarity with orchestration tools (e.g., Airflow, Prefect, Dagster). Experience with data storage systems (SQL / NoSQL databases, data lakes, object storage). Exposure to streaming or event-driven architectures. Experience deploying or operating ML systems in regulated or high-reliability environments. Familiarity with ML frameworks and scientific libraries (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow). Interest in applied AI topics such as NLP, LLM-based systems, or scientific computing.