Machine Learning Engineer
2 weeks ago
Key Responsibilities
- Develop and optimize machine learning models for structured data, including Regression, SVMs, Decision Trees, Random Forest, and XGBoost.
- Performing EDA and Feature Engineering
- Perform Model Fine-Tuning by leveraging techniques like Grid Search and Ensemble Methods .
- Implement unsupervised learning models such as K-Means and Gaussian Mixture Models .
- Utilize platforms like AWS SageMaker and tools like MLflow for model training, tracking, and deployment.
- Train and evaluate machine learning models from scratch using structured data.
- Work on RNNs and CNNs for structured data tasks as applicable.
- Leverage libraries like TensorFlow and PyTorch to implement machine learning pipelines.
- Collaborate with teams to integrate ML models into production systems.
- Analyze and preprocess large structured datasets to generate insights and drive decision-making.
Qualifications
- 3+ years of experience in data science with a focus on structured data and machine learning models .
- Proficiency in Python is a must-have (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
- Hands-on experience with machine learning techniques: Regression , SVMs , Decision Trees , Random Forest , and XGBoost .
- Experience with Feature Engineering techniques, including Grid Search and Ensemble Methods .
- Familiarity with unsupervised models like K-Means and Gaussian Mixture Models.
- Strong experience using AWS SageMaker and MLflow for ML workflows.
- Ability to train and optimize machine learning models from scratch .
- Solid understanding of machine learning techniques without focus on AI or LLMs.
- Knowledge of RNN and CNN frameworks for specific tasks.
Nice-to-Have Skills
- Experience with deployment pipelines for ML models.
- Familiarity with cloud platforms (e.g., AWS, GCP).
- Knowledge of SQL for structured data extraction and manipulation.
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