Data Science cum MLOps, Madrid (on-site) – International Client
Job role: Data Science cum MLOps.
Minimum experience: 6 to 8 years.
Studies required: Graduate.
Language: English (C1) (Mandatory).
Location: Madrid (on-site).
DESCRIPTION:
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).
Tasks:
- 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.
Specific Expertise:
- 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.
Nice to Have:
- 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.
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