To join our Sonae Data Team at the Holding Office, we are currently looking for a:

 MLOps Engineer

Sonae Data is a brand-new unit within the Sonae group, bringing together data from across the group’s diverse businesses—think supermarkets, electronics, financial services, telecom, shopping centers, and digital platforms. The goal? To turn this data into valuable products and services, first for the group’s own companies, and later for the broader digital and AI-driven market.

We’re looking for an MLOps Engineer to help bring this vision to life. In this role, you’ll build and optimize the infrastructure that powers our machine learning systems — ensuring that models move seamlessly from experimentation to reliable production. Working closely with data scientists, engineers, and business stakeholders, you’ll automate pipelines, streamline deployments, and monitor model performance to keep our AI solutions scalable, robust, and impactful. You’ll play a key role in enabling data-driven innovation, helping the organization turn cutting-edge models into real-world value with speed, stability, and confidence.

Core Responsibilities:

• Develop and maintain MLOps pipelines for model training, deployment, and monitoring in collaboration with data scientists and engineers;
• Automate repetitive ML workflows, ensuring efficiency, scalability, and reproducibility across environments;
• Support model deployment to production, including integration with APIs, batch jobs, and inference systems (real-time or batch);
• Implement monitoring and alerting systems to track model health, data quality, and performance drift;
• Collaborate closely with data platform engineers to ensure robust, reliable, and well-structured data flows for ML applications;
• Contribute to CI/CD processes for ML projects, ensuring proper testing, version control, and governance;
• Adopt and promote best practices in MLOps and LLMOps, focusing on automation, observability, and maintainability;

Main requirements:

• Bachelor’s or master’s degree in Computer Science, Engineering, Mathematics, or a related field;
• 2–5 years of experience in machine learning or data engineering roles, with at least some exposure to model deployment and monitoring;
• Proficiency in Python (Pandas, Scikit-learn, etc.);
• Experience with cloud services (AWS, GCP, or Azure)
• Experience with MLOps tooling (MLflow, Vertex AI, etc.);
• Knowledge of DevOps practices, including CI/CD, Git, Docker, and Kubernetes;
• Familiarity with data pipelines and workflow orchestration tools (Airflow, Glue, DataFactory, etc.);
• Strong analytical mindset and ability to troubleshoot ML production issues effectively;
• Good communication skills, able to explain technical topics clearly to both technical and non-technical stakeholders;
• Experience managing complexity and ambiguity with autonomy;
• Curiosity and adaptability, with a desire to learn emerging tools and MLOps practices.
• Curiosity, creativity, and a drive to keep learning

Work Location:

·       Hybrid work model

·       Role based in Oporto (preferably) or Lisbon

·       If based in Lisbon, regular travel to the Oporto headquarters will be required.