Blueridge Global

Design, build, and deploy production-grade forecasting models Develop end-to-end machine learning pipelines for data ingestion, feature engineering, model training, and deployment Build probabilistic models to quantify forecast uncertainty Write clean, modular, object-oriented code following best practices Implement MLOps best practices including model versioning, monitoring, and CI/CD Work with distributed training frameworks for scalable model development Collaborate with cross-functional teams (engineering, product, strategy) to align solutions with business needs Work with cloud-based environments (e.g., Databricks, AWS, GCP) for large-scale data processing Essential Skills & Qualifications 3 6 years of relevant experience in Data Science / ML Engineering Strong proficiency in Python and core libraries such as pandas, numpy, scikit-learn Hands-on experience with deep learning frameworks (preferably PyTorch) Experience with distributed training and large-scale datasets Proven ability to build scalable, production-grade ML pipelines Familiarity with MLOps practices (CI/CD, monitoring, retraining pipelines) Solid understanding of statistical modeling, regression, and time series Experience working in cloud environments (Databricks, AWS, GCP) Strong software engineering skills with a focus on clean, modular, object-oriented code Excellent problem-solving, communication, and collaboration skills