The IT Technical Engineer Act as a technical software expert, accountable for software development including the research, design, programming, and testing of operating or new software. Supporting business and IT to adapt digital technologies faster and to improve application or solution performance.

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.

 

           Responsibilities:

Design, implement, and execute test cases for AI/ML models. Conduct performance, stress, and regression testing for models. Ensure data quality and validation for training and testing datasets. Test model robustness to ensure reliability in various edge cases. Collaborate with Data Scientists and AI Engineers to ensure the quality of model outputs. Implement automated testing frameworks specific to AI/ML applications. Conduct ongoing monitoring and reporting of model performance post-deployment. Validate the fairness and compliance of models with ethical and regulatory standards.