data
Posted May 4AI Engineer
at Everfield
Spain Madrid, SpainRemote
Requirements
- What are we looking for? We are seeking a proactive tech enthusiast with the ability to work autonomously to lead our AI & Data department.
- Degree in Computer Science, Mathematics, or similar. At least 3 years of
- experience in this role or as a Data Scientist.
- Ability to understand processes and identify optimization opportunities.
- Knowledge of optimization techniques, model evaluation, and performance metrics. Solid
- experience in Traditional Machine Learning.
- Experience in demand forecasting within the retail/hospitality sector.
- Experience working with Big Data (massive datasets).
- A professional capable of working independently as the sole lead for the AI & Data area. What will your responsibilities be?
- Training, validating, and optimizing Machine Learning and Deep Learning models.
- Integrating Generative AI and LLMs into various solutions.
- Data analytics using Business Intelligence tools such as Apache Superset. Technical
- Requirements: Data Science & Machine Learning
- Solid foundation in mathematics and statistics.
- Expert knowledge of machine learning models and analytical/mathematical modeling.
- Statistical knowledge of Time Series and forecasting evaluation metrics.
- Advanced proficiency in Python (OOP best practices, testing, Pandas, PySpark, NumPy, Scikit-learn, LightGBM / XGBoost / Catboost, Matplotlib, TensorFlow, Hugging Face).
- Graph analysis and algorithm development. Generative AI
- Knowledge of the state-of-the-art in Language Models and GenAI.
- Agentic AI Architecture: agent loops, tools, MCPs, and Python agentic libraries.
- Strong mastery of SQL and query optimization.
- Knowledge of the Big Data ecosystem (Spark, Glue, Redshift, etc.).
- Airflow: Ability to build DAGs to orchestrate data flows.
- Experience handling Big Data file formats such as Parquet and DuckDB.
- Knowledge of AWS and basic data infrastructure (S3, Redshift, Bedrock, EC2, EKS, ECR).
- Docker: Containerization for service deployment.
- Kubernetes: Ability to operate and interact with clusters.
- Scalable architectures for ML operations.
- Proficiency with GIT version control.