DATA Engineer

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.

• 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.

• 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.