> Markdown version of [/jobs/ext/2130102-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2130102-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Capitole - **Location:** A Coruña, Spain - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Continuous Integration, Python (Programming Language), Machine Learning, Software Engineering, Data Processing, Pytorch, Large Language Models, Generative AI, Git, Pandas, Scikit Learn, Docker - **Published:** August 19, 2026 - **Apply:** https://www.buscojobs.com.es/machine-learning-engineer-en-la-coruna-ID-367858580 ## About the Role 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.Designing end-to-end NLP pipelines - data processing, model development, evaluation, deployment.Getting hands-on with LLMs, embeddings and modern GenAI tooling (OpenAI, AWS Bedrock). 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). ## Description Capitole keeps growing - and we want to do it with you!¡Inscríbase sin demora!Se espera un gran volumen de solicitantes para el puesto que se detalla a continuación, no espere para enviar su CV.We're looking for an AI/ML Engineer to 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.Designing end-to-end NLP pipelines - data processing, model development, evaluation, deployment.Getting hands-on with LLMs, embeddings and modern GenAI tooling (OpenAI, AWS Bedrock).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).Why CAPITOLE?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.Private health insurance fully paid by Capitole.Flexible compensation: meal, transport and/or childcare vouchers.WellHub (Gymforless).Discounts on major brands for employees (Club Capitole).xbhjioeDon't know us yet?Come discover us!()See what people say about us Glassdoor ()Capitole | Empowering people, unlocking technology innovation Hay opciones de teletrabajo/trabajo desde casa disponibles para este puesto. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Getting to Know Your Legacy (System) with AI-Driven Software Archeology](https://www.wearedevelopers.com/videos/1437-getting-to-know-your-legacy-system-with-ai-driven-software-archeology) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)