Data Science and MLOps Engineer

· Experience: 6-8 years in Machine Learning Engineering or Applied ML, with strong exposure to MLOps.

· Programming: Advanced proficiency in Python (OOP) and PySpark.

· ML Frameworks: Hands-on experience with Scikit-learn, TensorFlow, or PyTorch.

· Cloud & Platforms: Strong experience with Azure Cloud and Databricks.

· MLOps & Pipelines: Expertise in building ML pipelines using MLflow, Azure ML, and CI/CD tools (Azure DevOps).

· Data & Modeling: Strong knowledge of data preprocessing, feature engineering, model optimization, and evaluation techniques (cross-validation, A/B testing).

· Version Control: Experience with Git and collaborative development practices.

· Knowledge of Generative AI frameworks (e.g., LangChain).

· Familiarity with vector databases.

· Experience with model monitoring and logging in production.

· Understanding of data governance and compliance.

· Relevant Databricks or Azure certifications.

Studies required: Graduate.

Language: English (C1) (Mandatory).

· English (C1).

· 285-304 €/day.

We are looking for a Data Science & MLOps Engineer to join our Advanced Analytics & AI team. This role focuses on designing, developing, and deploying scalable machine learning and Generative AI solutions within an Azure-based ecosystem.

You will collaborate with data scientists, data engineers, and business stakeholders to deliver end-to-end ML pipelines and production-ready AI models. The role requires a strong balance between data science expertise and MLOps practices, ensuring robust, scalable, and maintainable solutions.

The position involves working in Agile/DevOps environments and contributing to innovation initiatives that leverage emerging technologies such as GenAI and cloud platforms (Databricks, Azure).

· Design, develop, and deploy machine learning and Generative AI models for advanced analytics use cases.

· Build and maintain end-to-end ML pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.

· Collaborate closely with data scientists, data engineers, and analysts to deliver scalable AI solutions.

· Implement and manage MLOps best practices, ensuring model reproducibility, monitoring, and lifecycle management.

· Optimize models for performance and scalability in production environments.

· Work with tools such as MLflow, Azure Machine Learning, and Azure DevOps for pipeline orchestration and CI/CD.

· Drive innovation by expanding AI use cases using emerging technologies such as Generative AI.

· Communicate complex analytical concepts to non-technical stakeholders and guide decision-making.

· Participate in Agile/Scrum teams, contributing to continuous delivery and iterative product development.

· Stay updated on industry trends in AI, MLOps, cloud computing, and data platforms.

Location: Madrid (on-site).

· Madrid (on site).

Data Science cum MLOps, Madrid (on-site) - International Client

Job role: Data Science cum MLOps.

Minimum experience: 6 to 8 years.