Job Title

ML Engineer – Structured Data & Machine Learning Location: Hyderabad, India - Hybrid Remote (3 days a week onsite) 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.