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.
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.
Soft skills
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.
You feel identified with this technical knowledge:
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.
Salary range: 30k-40k gross salary
Wellness support: An extra €55 gross per month to spend on wellness sessions or activities. ️
Remote work allowance: An extra €30 gross per month to help cover your home office expenses.
Private medical insurance included: Fully covered by Cigna ️. Plus, special rates if you want to add your family members.
Flexible remuneration: Optimize your salary with ticket transport, restaurant, and kindergarten vouchers via Cobee.
Continuous learning & development: We heavily invest in your personal and professional growth in different ways:
Flexibility & work-life balance:
Core hours: From 10:00 h to 16:00 h (Monday to Thursday), and 10:00 h to 14:00 h on Fridays. The rest of your working day is flexible!
30 days of remote work per year from a different location than your usual one.
Up for a virtual coffee?
