Data Engineer Mle

Insud Pharma; España; On-site; PHP 3,991,000 - 6,531,000; Full time

Benefits offered by this job

Flexible start time

Permanent contract

Life & accident insurance

Meal vouchers

Language learning platform

Wellness platform with psychologist

Cabify transportation service

Development plans

Many more!

Job summary

Insud Pharma's AI Labs team in Madrid is seeking a Data Engineer / Machine Learning Engineer to design, build, and operate end-to-end data pipelines and ML systems. You will collaborate with data scientists, software engineers, and product owners to move models from experimentation to production.

The role emphasizes scalable pipelines, MLOps, and robust engineering standards, with opportunities to explore NLP and scientific computing while maintaining code quality, versioning, and monitoring in

Qualifications

• 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.

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 behaviour.
• 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.

Skills

Python

REST APIs

Docker

MLOps

Airflow

NumPy

Pandas

Scikit-learn

PyTorch

TensorFlow

Git

Tools

FastAPI/Flask

Docker

Airflow

Dagster

Prefect

Job description

Data Engineer MLE - INSUD PHARMA

Madrid (Chamberí area).

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 are the AI Labs - the applied AI team at Insud Pharma, a team of 30 people including AI Engineers, Data Scientists, DevOps Engineers, and Product Managers building the system 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.

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 behaviour.
• 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

• 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.

Benefits

• Flexible start time from Monday to Friday
• Permanent contract.
• Life and accident insurance.
• Ticket restaurant.
• Training and language learning platform.
• Wellness platform with unlimited free psychologist sessions.
• Cabify transportation service for employee use.
• Development plans, internal mobility policy.
• Many more!

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