Ai/Ml Engineer

Momentum Reim
Barcelona, Spain
22 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours

Tech stack

Artificial Intelligence Algorithm Design Amazon Web Services Artificial Neural Networks Computer Vision Microsoft Azure Big Data Data Governance High-Level Architecture Machine Learning Tensorflow Google Cloud
+9 more
Cloud Platform System Feature Engineering Pytorch Deep Learning Data Strategy Scikit Learn Information Technology Machine Learning Operations Software Version Control

Job description

AI/ML EngineerAunque la experiencia profesional y las cualificaciones son clave para este puesto, asegúrese de comprobar si posee las habilidades interpersonales preferibles antes de solicitar, si se requieren.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 doLead 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.Experience / Who you areBachelor’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.xkdbapoExperience with data quality systems and AI ethics/qualification frameworks (e.g., ISO standards or industry?specific regulations).#J-*****-Ljbffr

Requirements

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. xkdbapo Experience with data quality systems and AI ethics/qualification frameworks (e.g., ISO standards or industry?specific regulations). #J-*****-Ljbffr

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