Are you an Azure Machine Learning Engineer seeking a new interesting challenge? If your answer is yes, it’s your lucky day so keep reading — it can be just what you're looking for! 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 Dev Ops 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), Py Spark Machine Learning frameworks: Scikit-learn, Tensor Flow, Py Torch Databricks platform tools MLflow and ML pipeline orchestration tools Git and CI/CD pipelines for ML models (Azure Dev Ops / 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,



Py Spark 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 Dev Ops Ability to work with multiple stakeholders in complex environments Good to have Familiarity with Generative AI models and open-source Gen AI frameworks such as Lang Chain 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? 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 adaptable 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. And so on – Appreciations, incentives, team building activities, diversity & inclusion programs, sustainability initiatives and corporate events… This has only just begun! #J-18808-Ljbffr

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