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.
ResponsibilitiesDefine 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
Requirements8+ 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
BenefitsHealth 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.
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