9 publicaciones de empleo destacadas en data science/mlops engineer (¡Están contratando!)

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MULTIVERSE COMPUTING —Donostia-San Sebastián, Guipúzcoa provincia
- Español
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Senior Director, AI Cloud & Compute Services
AstraZeneca —Barcelona, Barcelona provincia
- Jornada completa
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Lead AI Engineer, Business Acceleration
SGS —Madrid, Madrid provincia
- Jornada completa
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Grupo Lanit —España
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- Teletrabajo
- Azure
- Máster
- AWS
- Seguro médico privado
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Docplanner —España
- Jornada completa
- Teletrabajo
- Microsoft Word
- SQL
- Machine learning
- Abono gimnasio
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Fortra, LLC —España
- Jornada completa
- Teletrabajo
- Doctorado
- Java
- Máster
AI Engineer Senior
Kairos Digital Solutions —España
- Jornada completa
- Teletrabajo
- Inglés
- Bases de datos
- SQL
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Data Science cum MLOps, Madrid (on-site) – International Client

The White Team -
Tres Cantos, Madrid provincia

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Detalles del empleo

Hace 10 horas

Cualificaciones

TensorFlow
CI/CD
Azure
DevOps
Git
Inglés
Programación orientada a objetos
Machine learning
Scrum
Escalabilidad
Cloud
Python

Descripción completa del empleo

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.

Language:

English (C1).

Location:

Madrid (on site).

Rate:

285-304 €/day.

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