Machine Learning / MLOps Engineer – GCP and Python

• Solid and demonstrable experience with Google Cloud Platform (GCP), especially Vertex AI and BigQuery.

• Advanced level of Python, with experience in developing modular, testable code prepared for concurrent execution.

• Experience developing Machine Learning models with algorithms such as LightGBM, XGBoost, CatBoost, or similar.

• Experience working with large volumes of data and optimizing training and inference times.

• Experience in MLOps, taking models from experimental environments to production.

• Practical knowledge of Docker, CI/CD, and deployment automation.

• Experience building efficient data pipelines for training and inference processes.

• Software engineering best practices: Git, automated testing, code reviews, and dependency management.

• Experience in forecasting and time series, especially in demand forecasting with a high number of products or entities.

• Knowledge of seasonality, promotions, events, and cold-start strategies for new products.

• Experience with modern Deep Learning architectures for time series: Temporal Fusion Transformer (TFT), TiDE, PatchTST, TimesFM, Chronos, or Transformers.

• Knowledge of Generative AI, LLMOps, RAG, agents, model evaluation, and observability.

• Experience optimizing hyperparameters with Optuna, Ray Tune, or similar tools.

• Knowledge of training parallelization, inference optimization, and reduction of computational costs.

At Tecdata Engineering, we are looking for a Machine Learning Engineer specialized in Google Cloud Platform (GCP), Python, and MLOps to join a project for Grupo DIA.

We are looking for a technical profile with experience in the development of predictive models, processing large volumes of data, and the industrialization of Machine Learning solutions in production environments. The selected person will participate in the design, development, and optimization of data pipelines and models, ensuring their scalability, efficiency, and maintainability.

• Design, develop, and optimize Machine Learning solutions on Google Cloud Platform.

• Work with Vertex AI for the creation of pipelines, model lifecycle management, Model Registry, Workbench, and production deployments.

• Develop advanced code in Python following software engineering best practices, modularity, decoupling, and testing.

• Design and train predictive models with algorithms such as LightGBM, XGBoost, CatBoost, or other similar ones.

• Process and analyze large volumes of data, working with datasets of millions of records.

• Build and maintain automated training and inference pipelines, optimizing ETL/ELT processes.

• Industrialize models through MLOps practices, including versioning, reproducibility, automation, and monitoring.

• Implement CI/CD processes and manage runtime environments using Docker and tools such as GitHub Actions or GitLab CI.

• Optimize the performance of queries and data processes using BigQuery.

• Collaborate with technical teams applying development methodologies, code reviews, and version control.

Location: Las Rozas, Madrid (Calle Jacinto Benavente)
Modality: Hybrid, with office attendance 1 day a week.