BSc. or MSc. degree in Computer Science, Engineering, Data Science, or a related field.
Minimum of 5 years of hands-on experience in machine learning engineering, with a strong focus on productionizing models and building ML infrastructure.
Proficiency in Python, with experience in Java, Scala, or similar programming languages.
Experience with ML and DL frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
Strong understanding of CI/CD best practices and experience with tools like Jenkins, GitLab CI, or similar.
Knowledge of A/B testing frameworks and model validation techniques.
Proficiency in building and maintaining data processing pipelines using tools like Apache Spark, Airflow, or Kafka.
Familiarity with cloud services (e.g., AWS, GCP, or Azure) and containerization tools (e.g., Docker, Kubernetes).
Experience building APIs with frameworks such as Streamlit, Flask, or FastAPI for model deployment and interaction.
Strong understanding of software engineering principles, including object-oriented programming, version control, and testing frameworks.
Experience in developing scalable and maintainable code for real-world applications.
Excellent problem-solving abilities and analytical mindset.
Strong communication skills, both written and verbal.
Ability to work collaboratively in an Agile environment with cross-functional teams.