Strong systems-level problem-solving skills, with the ability to balance performance, scalability, maintainability, and business impact.
Excellent communication skills, with the ability to clearly articulate complex technical problems and solutions to both engineers and business stakeholders.
Nice to have:
PhD in AI, Machine Learning, or a related field.
Experience on Computer Vision (CV).
Excellent engineering skills: Experience bringing ML models into production with best practices for observability, monitoring, and performance.
Experience with CI/CD pipelines and commonly used tools in ML Engineering such as Metaflow, MLflow, Airflow, Grafana, and similar.
Experience with large-scale ML model validation, experimentation, and A/B testing.
Hands-on experience with major cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
What You'll Be Doing:
Design and build resilient ETL/ELT pipelines to ingest, clean, and validate image data and associated metadata (e.g., vendor catalog features).
Work with the Data Scientist to create and maintain the "Ground Truth" dataset necessary for calibrating the third-party model and benchmarking the accuracy.
Build reliable, production-grade feature pipelines to ensure data consistency between model training and live inference.
Build automated infrastructure to support model training cycles with a focus on data versioning and reproducibility.
Contribute to the long-term vision of product image recognition while delivering short-term wins for measurable business impact.
Perks and Benefits:
Monthly Glovo credits
Discounted gym memberships
Extra time off, work from home flexibility, and opportunities to work from anywhere