AI Engineer

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.