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We are seeking a highly skilled Machine Learning Engineer with at least 5 years of hands-on experience in designing, developing, deploying, and maintaining machine learning models. The ideal candidate should have a strong foundation in data science, a proven track record of solving real-world problems using ML, and a passion for building scalable and production-grade ML pipelines. Key Responsibilities: • Design, build, and deploy end-to-end machine learning solutions tailored to business problems. • Perform data preprocessing, feature engineering, and model selection for various types of structured and unstructured data. • Develop, train, validate, and fine-tune machine learning models including regression, classification, clustering, recommendation systems, and NLP-based models. • Build automated model training and evaluation pipelines using industry best practices (e.g., MLFlow, Airflow, Kubeflow). • Collaborate with data engineers, product teams, and stakeholders to translate requirements into technical implementations. • Evaluate model performance using appropriate metrics (e.g., precision, recall, AUC, RMSE) and apply techniques like hyperparameter tuning, ensembling, and cross-validation. • Deploy models to production using containerized environments (e.g., Docker, Kubernetes) and integrate with web services/APIs. • Continuously monitor model performance and implement model retraining strategies to handle data drift or changing business conditions. • Document code, workflows, and research findings in a clear, concise, and reproducible manner. • Stay up to date with the latest ML research and technologies and recommend improvements or new approaches. Required Skills & Qualifications: • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related field. • 5+ years of hands-on experience developing and deploying ML models in production environments. • Proficient in Python (with libraries like scikit-learn, pandas, NumPy, TensorFlow, PyTorch). • Solid understanding of statistical modeling supervised and unsupervised learning techniques. • Experience with data pipelines and orchestration tools such as Airflow, Prefect, or Luigi. • Hands-on experience with cloud platforms (AWS, GCP, Azure) and MLOps frameworks. • Knowledge of SQL and experience working with large-scale datasets in relational and NoSQL databases. • Familiarity with version control systems (e.g., Git) and containerization tools (Docker, Kubernetes). Preferred (Good-to-Have): • Experience with NLP, Computer Vision, or Time Series Forecasting models. • Familiarity with distributed computing frameworks such as Spark or Dask.