Are you an Azure Machine Learning Engineer seeking a new interesting challenge?

Sea uno de los primeros solicitantes, lea la descripción completa del puesto a continuación y luego envíe su candidatura para que sea considerada.

If your answer is yes, it’s your lucky day so keep reading — it can be just what you're looking for!

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- Expand the scope of Advanced Analytics and AI, enabling use cases that leverage emerging technologies and fostering an innovative environment.

- Work closely with the Data Scientists of the Advanced Analytics & AI Center of Expertise to design and develop Machine Learning and Generative AI models, collaborating with data engineers and data analysts.

- Deploy and optimize AI models that leverage large-scale data to deliver predictive and analytical capabilities for the AST&I; domains.

- Build and maintain end-to-end ML pipelines, ensuring model reproducibility, scalability and monitoring aligned with MLOps best practices.

- Engage in complex long-term projects, focusing on continuous delivery in small increments and contributing to project planning and execution.

- Engage and guide non-technical stakeholders on Advanced Analytics and Generative AI use cases.

- Collaborate with multidisciplinary teams and manage multiple stakeholders.

- Be part of a diverse Agile / Scrum DevOps team with end-to-end responsibility for developing, managing and maintaining functionalities in the AST&I; area, prioritized by the Product Owner.

- Stay curious and up to date with trends in advanced analytics, generative AI and cloud platforms such as Databricks.

- Python (OOP preferred), PySpark

- Machine Learning frameworks: Scikit-learn, TensorFlow, PyTorch

- Databricks platform tools

- MLflow and ML pipeline orchestration tools

- Git and CI/CD pipelines for ML models (Azure DevOps / Azure Pipelines)

- MLOps practices (feature engineering, training, evaluation and deployment)

- Years’ experience:



5+ years in Machine Learning Engineering or applied ML with a focus on Azure cloud technologies

- Strong proficiency in Python, PySpark and modern ML frameworks

- Hands-on experience with the Databricks platform

- Solid understanding of data preprocessing, feature engineering and model optimization

- Experience orchestrating ML pipelines using tools such as MLflow and Azure Machine Learning

- Strong understanding of ML evaluation metrics and methods including A/B testing and cross-validation

- Experience with Git and building CI/CD pipelines for ML models, preferably in Azure DevOps

- Ability to work with multiple stakeholders in complex environments

Good to have

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- Familiarity with Generative AI models and open-source GenAI frameworks such as LangChain

- Knowledge of vector databases

- Experience with monitoring and logging ML models in production

- Knowledge of data governance and compliance for ML use cases

- Databricks certifications

Where and when?

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- Workplace: Madrid (hybrid model)

- Work Schedule: office hours

- Permanent contract – We offer indefinite contracts from the first day.

- Pay and benefits – Competitive salary and a versátil compensation plan adapted to your needs (Ticket Restaurant, Childcare Ticket, Transport Ticket and Health Insurance).

- Work from home – Monthly financial support for your working-from-home expenses, plus a welcome bonus in the first month to help you set up your workplace.

- Opportunity knocks – Career development plan and annual performance-based compensation reviews.

- Learn as you grow – Fantastic onboarding program and access to robust learning platforms.

- Bring your buddy – Referral bonus through our BYB Scheme.

- Connect globally – Work with multicultural teams from all over the world.

- Benefit from being a TCSer – Corporate discounts and exclusive benefits. xcskxlj

- And so on – Appreciations, incentives, team building activities, diversity & inclusion programs, sustainability initiatives and corporate events… This has only just begun!

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📌 Azure Machine Learning Engineer (España)
🏢 Tata Consultancy Services
📍 España