Machine Learning Engineer

We are looking for an MLOps / ML Engineer to manage the full lifecycle of Machine Learning models within an advanced document-processing platform. The role focuses on deploying, distributing, and retraining Computer Vision models used for automated document analysis, OCR, and intelligent entity extraction. This person will work within a multidisciplinary team responsible for developing, improving, and operating a scalable document-processing solution. Unlike a pure DevOps-oriented MLOps role, this position is more hands-on in ML development, model optimisation, and fine-tuning, with a moderate MLOps component focused on pipelines, deployment, and automation., Manage the end-to-end lifecycle of Computer Vision and OCR-based ML models (deployment, optimisation, fine-tuning, retraining). Operate and improve data and ML pipelines using Airflow and MLflow. Deploy and maintain ML components within Databricks environments. Implement and maintain CI/CD pipelines for ML modules. Use Terraform or Ansible for IaC automation and environment provisioning. Work closely with Data Scientists and Software Engineers to ensure reliable, scalable delivery of document-processing models. Contribute to continuous improvement of the platform's AI capabilities.

Strong experience in Python and ML frameworks such as PyTorch or TensorFlow. Experience managing data pipelines and ML pipelines (Airflow, MLflow). Hands-on experience deploying AI modules in Databricks . Exposure to IaC tools such as Terraform or Ansible. Experience designing and maintaining CI/CD pipelines. Background in Machine Learning development, model fine-tuning, and optimisation. Spanish Mandatory Nice to Have Experience with OCR, Computer Vision, or document-processing models. Familiarity with scalable ML systems or automated retraining processes. Experience in environments requiring both ML engineering and MLOps competencies