Data Engineer / MLE for AI Pipelines — Madrid

Insud Pharma; O Chazo; Presencial; EUR 60.000 - 90.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flexible start time

Life and accident insurance

Ticket restaurant

Training and language platform

Wellness platform with psychologist

Cabify transportation service

Descripción de la vacante

INSUD PHARMA in Madrid hosts AI Labs, a dedicated applied AI team building systems powering trials, recruitment, and monitoring. We seek a Data Engineer / MLE who blends software engineering with data pipelines and ML systems, collaborating with scientists, engineers, and product owners.

The role focuses on end-to-end data and ML deployment across Regulatory, Clinical Trials, R&D, and Manufacturing, ensuring reliable path from experimentation to production with scalable pipelines and solid

Formación

• Proficient in Python with clean, modular code and packaging skills.
• Strong experience building data pipelines and ML workflows.
• Hands-on with REST APIs and service-oriented architectures.
• Experience with CI/CD, Git workflows, and containerization.
• Familiarity with MLOps practices, model versioning, monitoring, and retraining.
• Experience with cloud environments (AWS/Azure) and orchestration tools.

Responsabilidades

• Design, build, deploy, and operate end-to-end data and ML solutions.
• Collaborate with data scientists to translate experiments into production.
• Develop scalable data pipelines and ML workflows for multiple domains.
• Implement robust deployment patterns (batch/real-time) and monitoring.
• Contribute to data engineering and MLOps standards and best practices.

Conocimientos

Python

REST APIs

Git

Airflow

MLOps

Herramientas

Docker

AWS

Azure

FastAPI

Flask

NumPy

Descripción del empleo

INSUD PHARMA in Madrid hosts AI Labs, a dedicated applied AI team building systems powering trials, recruitment, and monitoring. We seek a Data Engineer / MLE who blends software engineering with data pipelines and ML systems, collaborating with scientists, engineers, and product owners.

The role focuses on end-to-end data and ML deployment across Regulatory, Clinical Trials, R&D, and Manufacturing, ensuring reliable path from experimentation to production with scalable pipelines and solid