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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer H/F - **Company:** Capitole - **Location:** Valladolid, 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-h-f-en-valladolid-ID-367931880 ## About the Role 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. 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). 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). ? 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. Hybrid: 1-2 days in the office). ? Language : English C1 (Fluent, all team communication is in English). ## Description 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.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).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).? 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.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.Flexible working hours to balance your professional and personal life.?? Private health insurance fully paid by Capitole.?? Discounts on major brands for employees (Club Capitole).? Capitole | Empowering people, unlocking technology innovation ## 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) - [How E.On productionizes its AI model & Implementation of Secure Generative AI.](https://www.wearedevelopers.com/videos/623-how-e-on-productionizes-its-ai-model-implementation-of-secure-generative-ai) - [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) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)