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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Model IA Engineer - **Company:** Fluendo - **Location:** Barcelona, Spain (Remote available) - **Salary:** €30,000.0 - €40,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Amazon S3, Data Analysis, Bash Shell, Cloud Computing, Cloud Storage, Program Optimization, Continuous Integration, Github, Python (Programming Language), OpenCV, Raw Data, Tensorflow, SQL Databases, Management of Software Versions, Pytorch, Pytest, Gitlab-ci, Git Flow, Information Technology, ONNX (Open Neural Network Exchange) Format, HuggingFace, Machine Learning Operations, Docker - **Published:** July 18, 2026 - **Apply:** https://es.indeed.com/viewjob?jk=b8963ddb7351870a ## About the Role A meticulous approach to testing and documenting results. Ability to identify bottlenecks and propose technical solutions. Skilled at translating complex model behaviors into clear reports and collaborating across teams. A continuous learning mindset and receptiveness to peer feedback. Experience 3-6 years of experience in AI Model Engineering Degree in Computer Science, Data Science, Mathematics, or a related field. Deep understanding of the AI model lifecycle, MLOps fundamentals, and model optimization. Proficiency in both English and Spanish is required., * AI & Model Engineering * Frameworks: Advanced Python, PyTorch, and TensorFlow (specifically custom loops). * Libraries: HuggingFace (Transformers/Datasets), OpenCV, Albumentations. * MLOps & Tools: MLflow (experiment tracking), DVC (versioning), and Optuna/Ray Tune (hyperparameter tuning). * Optimisation: Model quantisation, pruning, and ONNX validation. * Data Analysis: Proficiency in pandas profiling, data drift checks, and bias analysis (distribution skew, label bias). * Environment: Docker, Bash scripting, and SQL (advanced queries). * CI/CD: GitFlow, GitLab CI, or GitHub Actions. * Cloud: Basic experience with AWS S3 or similar cloud storage. * Testing: Advanced usage of pytest and config-driven pipelines. * Multimedia: Basic knowledge of GStreamer development is a plus. ## Description As an AI Model Engineer, you will be responsible for the end-to-end lifecycle of our AI models. You will develop, train, evaluate, and optimize models and datasets, ensuring every experiment is reproducible and delivers measurable performance improvements. You will bridge the gap between raw data and production-ready intelligence. Day-to-day life Operational & Tactical Management * Train and fine-tune models independently using state-of-the-art frameworks. * Design rigorous experiment plans, including benchmarking and ablation studies, to drive model evolution and improvement. * Design dataset preparation, filtering, and versioning strategies and tools to ensure high-quality training data. * Define evaluation protocols, apply model optimisation techniques, and export models for specific production targets. * Maintain reproducible experiment pipelines and produce detailed technical evaluation reports. Strategic Contribution * Workflow Improvement by proposing and implementing enhancements to internal training and evaluation workflows. * Contribute to the definition of model engineering standards and review experiment pipelines developed by junior team members. * Coordinate with Production Engineers on model I/O and constraints, and support external technical demos or presentations. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Deepfakes in Realtime - How Neural Networks Are Changing Our World](https://www.wearedevelopers.com/videos/180-deepfakes-in-realtime-how-neural-networks-are-changing-our-world) - [pytest: Simple, rapid and fun testing with Python](https://www.wearedevelopers.com/videos/213-pytest-simple-rapid-and-fun-testing-with-python) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Unboxing the DeepFace](https://www.wearedevelopers.com/videos/335-unboxing-the-deepface) ## Related Articles - [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) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)