Descripción de la oferta
Company Description
We’re an early-stage company, already backed by top programs likeLanzadera, Madrid Food Innovation Hub, Basque Culinary Center, and EU Tech Funds, and recognised in competitions such as theFuture Gastronomy Startup CompetitionandPremio Emprendimiento Digital(Comunidad de Madrid).
We’re now entering a scaling phase, moving from pilots to real deployments — and building the infrastructure to support it.
You will join as a key early member of the tech team, working closely with the founders and acting as the second core technical profile, with ownership over data and ML infrastructure.
We’re looking for someone with around 3+ years of experience in data engineering, MLOps, or related roles, comfortable working in early-stage environments and taking ownership end-to-end.
We’re looking for a Data Engineer & MLOps Engineer to own and scale the data and ML infrastructure behind our platform.
You will be responsible for everything that happens between raw data and reliable AI in production.Design and build end-to-end data pipelines (from edge devices to cloud)
Deploy, version, and monitor machine learning models at scale
Build robust MLOps workflows (training → evaluation → deployment → monitoring)
Ensure data quality, reliability, and observability across the platform
Optimize pipelines for performance, scalability, and cost
Work with large-scale image data and real-time ingestion systems
Support the integration and improvement of machine learning and computer vision models (data preparation, evaluation, and iteration loops)
Contribute to improving model performance in production through better data, monitoring, and feedback pipelines
Strong experience with Python and data-intensive systems
Experience building and maintaining production data pipelines
Solid understanding of cloud infrastructure (GCP preferred, AWS also valid)
Hands-on experience with Docker and production deployments
Familiarity with MLOps concepts (model lifecycle, monitoring, reproducibility)
Experience with workflow orchestration tools (Airflow, Prefect, or similar)
Familiarity with Kubernetes or similar orchestration systems
Experience working with streaming or near real-time data systemsYou’ll have high ownership over critical infrastructure from early stage
You’ll help define how our data and ML platform is built from scratch
Full-time role
Hybrid setup (Madrid, ~2 days/week in office)
Spanish required
Flexible, outcome-driven work environment (we care about results, not hours)
