Associate - AI | ML Engineer
Tasks
- Apply model explainability techniques using SHAP values
- Build supervised machine learning models for regression and classification
- Collaborate with MLOps and engineering teams for model packaging and deployment
- Conduct feature engineering hyperparameter tuning cross validation and model selection
- Deploy and monitor machine learning models in production
- Design develop optimize prompts for large language models
- Evaluate model performance using RMSE MAE AUC ROC F1 and R2
- Monitor model drift data quality and performance degradation
- Participate in code reviews and document work
- Perform exploratory data analysis on structured and semi structured datasets
- Translate analytical findings into stakeholder narratives
Perks/Benefits
- N/A
Skills/Tech-stack
AUC-ROC | Cross-validation | Data Analysis | Decision Trees | Exploratory Data Analysis | F1-score | Feature Engineering | Git | Gradient boosting | Hyperparameter Tuning | LightGBM | Linear Regression | Logistic Regression | MAE | Machine Learning | Matplotlib | Model Drift | Model Explainability | Model Monitoring | Model selection | NumPy | Pandas | Partial Dependence | Partial Dependence Plots | Precision-recall | Python | R2) | RMSE | Random Forests | SHAP | SQL | Scikit-learn | Support Vector Machines | Vector machines | XGBoost
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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