AI Engineer

• Develop, train, and fine-tune machine learning models for classification, regression, and clustering tasks.
• Maintain and optimize data pipelines (ETL) to support model training and inference.
• Deploy models into production as RESTful APIs or microservices.
• Monitor model performance in production and implement retraining strategies.
• Collaborate with data engineers to ensure data quality and availability.
• Write clear, reproducible code and maintain version control (Git).
• Document model architecture, experiments, and deployment procedures.
• Support cross-functional teams (Product, QA, DevOps) in integrating ML solutions.

Mandatory

• Bachelor's degree in computer science, AI, Machine Learning, or related field.
• 3-5 years of experience as an AI Engineer
• 1–2 years of hands-on experience building ML models in a professional setting or via substantial projects.
• Proficient in Python and core ML libraries (NumPy, pandas, scikit-learn).
• Practical experience with at least one deep learning framework (TensorFlow or PyTorch).
• Familiarity with Docker and basic CI/CD workflows for ML.
• Strong understanding of software development best practices and code quality.
• Ability to analyze metrics, diagnose model drift, and optimize performance.
• Strong English communication skills and the ability to work effectively in a team.

Nice to have

• Experience with cloud platforms (AWS, GCP, or Azure) for ML workloads.
• Knowledge of MLOps tools (MLflow, Kubeflow, TFX, ...).
• Experience with big data ecosystems (Spark, Hadoop, ...).
• Exposure to NLP, computer vision, or recommendation systems.
• Contributions to open-source ML projects or participation in Kaggle competitions.
• Proven research track record with ≥1 publication in AI venues.