Data Engineer & ML Ops

Company Description Gambooza is a growing AI startup based in Madrid, building computer vision systems to help restaurants reduce food waste, tackle operational inefficiencies, and improve their long-term viability. We’re tackling a massive, overlooked problem: inefficiencies and food waste in food service. Our technology brings visibility into kitchen operations using AI, helping operators reduce costs and environmental impact. We’re an early-stage company, already backed by top programs like

Lanzadera, Madrid Food Innovation Hub, Basque Culinary Center , and EU Tech Funds, and recognised in competitions such as the

Future Gastronomy Startup Competition

and

Premio 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. If you want to work on real AI systems in production, own critical infrastructure, and help shape a company from the ground up, this is that kind of role.

Role We’re looking for a Data Engineer & MLOps Engineer to own and scale the data and ML infrastructure behind our platform. This is not a maintenance role — you’ll be building systems from scratch, making key architectural decisions, and working directly on production AI pipelines connected to real-world environments (kitchens, cameras, edge devices). You will be responsible for everything that happens between raw data and reliable AI in production.

What you´ll do Design and build end-to-end data pipelines (from edge devices to cloud) Own the infrastructure that powers our computer vision systems in production 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 Make foundational decisions on architecture, tooling, and infrastructure

What we are looking for 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) Strong engineering mindset: you care about reliability, scalability, and clean systems Comfortable working in ambiguity and taking ownership of problems end-to-end

Strong Plus Experience deploying ML models in production Experience with computer vision pipelines Familiarity with Kubernetes or similar orchestration systems Experience with tools like MLflow, Weights & Biases, or feature stores Experience working with streaming or near real-time data systems

What makes this role differente? You’ll work on real AI systems in production, not experiments Your work will directly impact how much food is wasted every day You’ll have high ownership over critical infrastructure from early stage You’ll help define how our data and ML platform is built from scratch You’ll be part of a small, high-impact team, where things move fast and ship often

Practical detailes & Perks Full-time role Hybrid setup (Madrid, ~2 days/week in office) Spanish required Flexible, outcome-driven work environment (we care about results, not hours) Competitive salary + phantom shares High ownership and autonomy from day one Flat organization with a small, highly talented team