Machine Learning Engineer - Barcelona, Cataluña, Spain

We’re looking for someone who: Brings 3-5 years of experience building production features/systems with AI/ML . Writes advanced, production-grade Python - not notebook scripting. Has worked on real NLP / GenAI / LLM projects and can explain them in depth (embeddings, evaluation metrics beyond accuracy, how they assessed system performance). Thinks like an ML engineer : moves models to production, understands the full pipeline, applies solid coding practices (Git, PR, CI/CD, Docker ). Knows their way around ML libraries (scikit-learn, PyTorch) and data processing (pandas). Nice to have: Experience with Databricks. Familiarity with AWS infrastructure (S3, Lambda, Bedrock). Understanding of data augmentation, bias and training pipelines . A Software Engineering background with a recent move into AI/ML. Location: Barcelona. (Hybrid: 1-2 days in the office). Language: English C1 (Fluent, all team communication is in English).

An individual training budget of €1,200 for whatever you choose: events, books, certifications or courses. Monthly check-ins with your team for continuous feedback. Flexible working hours to balance your professional and personal life. Flexible compensation: meal, transport and/or childcare vouchers. Discounts on major brands for employees (Club Capitole). Capitole | Empowering people, unlocking technology innovation #J-*****-Ljbffr

We’re looking for an Machine Learning Engineer #MLEto join a global leader in HR technology, a team building Generative AI assistants used across global markets. This is a hands-on engineering role , where focus is shipping GenAI/NLP models to production in Python , working shoulder-to-shoulder with MLEs inside a lean 5-6 person squad across 3-4 projects. If you code your ML solutions yourself rather than leaning on out-of-the-box AutoML tools, and you like owning the technical solution end-to-end , this is for you. What you’ll find here: Building production-ready GenAI/LLM features (chatbot assistants and NLP systems, from prototype to production ). Writing structured, quality Python with real engineering discipline: PR practices, Git, CI/CD, Docker .