Data Engineer MLE

In a few words

Position:

Data Engineer

MLE Location: Madrid (Chamberí area).

Want to know more?

INSUD PHARMA operates across the entire pharmaceutical value chain, providing specialized knowledge and experience in scientific research, development, manufacturing, sales, and marketing of a wide range of active pharmaceutical ingredients (API), finished dosage forms (FDF), and branded pharmaceutical products, adding value to human and animal health.

The activities of INSUD PHARMA are organized into three synergistic business areas: Industrial (Chemo), Branded (Exeltis), and Biotech (mAbxience), with over 9,000 professionals in more than 50 countries, 20 state-of-the-art facilities, 15 specialized R&D centers, 12 commercial offices, and more than 35 pharmaceutical subsidiaries, serving 1,150 customers in 96 countries worldwide. INSUD PHARMA believes in innovation and sustainable development.

Ready to be a #Challenger?

What are we looking for?

We're AI Labs — the applied AI team at Insud Pharma. 30 people. AI Engineers, Data Scientists, DevOps Engineers, Product Managers building the systems that power how trials get designed, how patients get recruited, and how everything gets monitored once the trial is live.

Clinical trials run on data. Bad pipelines, slow models, and infrastructure that breaks under pressure can cost months — or worse, the trial itself.

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.

How the team works:

AI Labs operates with a startup mindset within Insud Pharma. The department is young, and the culture reflects that:

flat, collaborative, and fast-moving

. You will work alongside Data Scientists, AI Engineers, DevOps Engineers, and Product Managers who are equally committed to delivering high-quality work. We hold regular

demo days

where teams present their work, as well as

whiteboard sessions

where we tackle problems together. The cross-disciplinary dynamic is genuinely strong. The office is located in central Madrid (Chamberí, near Eloy Gonzalo), well connected and situated in a vibrant part of the city.

The challenge

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.

What do you need?

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