We are looking for an experienced Machine Learning Architect to lead the design and implementation of scalable AI and ML solutions across modern cloud data platforms. This role combines architecture, engineering, and strategic leadership to enable enterprise-scale machine learning capabilities. The ideal candidate has strong hands-on experience with Databricks and a deep understanding of the ML lifecycle management, MLOps, scalable data architectures, and AI platform governance. This is a highly collaborative role working closely with Data Engineering, Data Science, Product, and Business stakeholders to design robust, scalable, and production-ready AI solutions. Responsibilities Define and lead the architecture for scalable Machine Learning and AI platforms Design end-to-end ML workflows using Databricks, including: Feature engineering, Model training, Experimentation, Deployment, Monitoring Architect scalable data pipelines for AI/ML workloads using: Apache Spark, Python, SQL Establish MLOps best practices including: CI/CD for ML, Model versioning, Model governance, Automated retraining, Model drifting, Observability and monitoring Design secure and compliant AI architectures aligned with governance and privacy standards Partner with Data Engineering teams to optimize data models and feature stores Guide Data Scientists and ML Engineers on scalable production design patterns Evaluate and integrate modern AI capabilities, including (this will be a plus): LLMs, Vector databases, Retrieval augmented generation (RAG), AI agents Drive cost optimisation, scalability, and operational excellence across ML platforms Define reference architectures and best practices across multiple ML teams (not just owning a single project) Support stakeholder engagement and translate business needs into scalable technical solutions Requirements 8+ years in Data, AI or Machine Learning Engineering roles 3+ years designing ML platforms or AI architecture at scale Strong hands-on experience with: Databricks Apache Spark Python SQL Strong understanding of: MLOps ML lifecycle management Distributed ML systems Feature engineering Model deployment patterns Databricks Unity Catalog, Delta Lake and Lakehouse architecture experience Experience with cloud platforms (AWS, Azure, or GCP) Experience deploying ML models into production environments Strong knowledge of data architecture and scalable ETL/ELT patterns Experience working with orchestration frameworks such as Apache Airflow Strong stakeholder communication and technical leadership skills Benefits Health Insurance Flexible working hours Open holidays – take the time you need for yourself Profit distribution for everyone Annual Mindera trip, sports and sharing groups to connect and have fun Training and conferences – create your own training plan Child Care vouchers Choose laptop and peripherals that best suit your needs Hotspot with unlimited usage for work or leisure Snack provisions at the office Partnerships with local businesses Locations: Porto, Aveiro, Coimbra, Portugal – remote options may be considered based on your location. #J-*****-Ljbffr