Skills Required:

Typically 1-2 years of professional experience.

. Programming Languages

• Python: Widely used for machine learning, automation, and data analysis tasks.
• R: Popular for data analysis and statistics, commonly used in machine learning.

2. Machine Learning Concepts

• Supervised, Unsupervised, and Reinforcement Learning: Understanding these core techniques is essential.
• Model Evaluation Metrics: Knowledge of metrics like accuracy, precision, recall, F1-score, ROC-AUC, etc.
• Model Overfitting and Underfitting: Understanding how to test for and mitigate these issues.
• Feature Engineering: Knowledge of how to create meaningful features for ML models.

3. Testing Knowledge

• Test Automation: Proficiency in creating test scripts for AI/ML models to validate the functionality, performance, and accuracy of models.
• Unit Testing: Writing unit tests for machine learning pipelines and algorithms.
• Integration Testing: Ensuring the integration of different components like data processing pipelines, model training, and prediction services work as expected.
• Regression Testing: Ensuring that new code does not break existing functionality.
• Performance Testing: Validating the efficiency of models and ensuring that they perform well in real-world environments.