AI/ML Engineer

We are seeking a highly motivated AI/ML Engineer to join our team in Spain. This is a permanent position offering the opportunity to work at the forefront of artificial intelligence and machine learning innovation.

The successful candidate will play a key role in the development, training, and optimization of models, with a focus on enhancing algorithmic performance and predictive accuracy for demanding engineering applications. You will be part of a multidisciplinary team developing intelligent systems and automated solutions from concept through to production. This role offers long\-term career growth and the opportunity to shape the digital transformation and data\-driven strategy of innovative platforms.

All applicants must be eligible to work in Spain.

Responsibilities / What you will do

• Lead and support the development of new machine learning models, with an emphasis on deep learning, neural networks, and advanced algorithms.


• Characterize and optimize model performance metrics (e.g., accuracy, precision, recall, latency, and scalability).


• Conduct experimental testing and analysis of large datasets, feature engineering, and model architectures.


• Collaborate with software engineers and product teams to ensure model performance aligns with end\-use applications and production environments.


• Develop and maintain ML pipelines, data standards, and automated testing frameworks for model qualification.


• Support R\&D projects, contributing to technical reports, publications, and presentations on AI advancements.


• Stay current with emerging AI research, frameworks, and industry standards in machine learning and data science.

• Bachelor’s degree (or higher) in Computer Science, Data Science, Mathematics, or a related field.


• Proven experience in the development and deployment of machine learning models (e.g., NLP, computer vision, or predictive analytics).


• Strong background in statistical analysis and algorithmic characterization.


• Hands\-on experience with ML testing techniques and frameworks (e.g., PyTorch, TensorFlow, Scikit\-learn).


• Ability to design and execute data experiments, interpret complex datasets, and draw actionable conclusions.


• Proficiency in technical reporting and the ability to communicate AI results to both technical and non\-technical stakeholders.


• Knowledge of MLOps practices, model versioning, and process–data interactions.


• Familiarity with cloud computing platforms (AWS, Azure, GCP) or simulation tools for testing model behavior.


• Experience with data quality systems and AI ethics/qualification frameworks (e.g., ISO standards or industry\-specific regulations).

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