Join a cross-functional ML engineering team building the pipelines, feature stores, and serving infrastructure that take models from experimentation into production.
Collaborate daily with data scientists, product engineers, and platform engineers in an environment where ML is a core capability, not a side project.

3–8 years of combined experience in data engineering and ML model lifecycle work (Mid: 3–5 yrs / Senior: 5–8 yrs)
Advanced, production-grade Python skills — model training, serving, and automation
Hands-on experience with at least one ML framework: scikit-learn, PyTorch, or TensorFlow
Experience with MLOps tooling: MLflow, Kubeflow, or Amazon SageMaker for experiment tracking and model serving
Solid understanding of the end-to-end ML lifecycle : data ingestion feature engineering training evaluation production
Experience designing and owning feature stores for both batch and real-time inference
Model deployment and REST or gRPC serving patterns in production environments
Hands-on with cloud ML services: Amazon SageMaker, GCP Vertex AI, or Azure ML
Data pipeline tooling: Apache Airflow, Apache Spark, or dbt
CI/CD for ML — model registry management, A/B testing frameworks, and production monitoring
Strong SQL and data wrangling skills at scale
English: B2 (Upper Intermediate) — mandatory
Based in Portugal — mandatory. Portuguese nationals or residents strongly preferred.

Data & ML Engineer – Python, PyTorch, MLflow

Key skills