<h3><strong>Job Description: Senior Machine Learning Engineer </strong></h3><p><strong>Experience:</strong> 6–8 Years</p><p><strong>Employment Type:</strong> Full-time</p><p><strong>Location:</strong> Hyderabad/On- Site</p><p><br /></p><h3><strong>Role Summary</strong></h3><p>We are seeking a Senior Machine Learning Engineer to design and build intelligent decision-making systems using time-series forecasting, spatial modeling, and optimization techniques. The role involves evolving systems from rule-based logic to advanced machine learning and reinforcement learning, and deploying them at production scale.</p><p><br /></p><h3><strong>Key Responsibilities</strong></h3><ul><li>Build time-series demand forecasting models using rolling statistics, classical ML, and deep learning</li><li>Develop spatial / geospatial models using hexagonal or grid-based representations (e.g., H3)</li><li>Implement classification models (logistic regression, tree-based models) for risk and state detection</li><li>Design and optimize scoring and ranking engines using weighted heuristics and learned value functions</li><li>Work on policy learning and reinforcement learning for long-horizon optimization</li><li>Own feature engineering, training pipelines, and model evaluation</li><li>Deploy models for batch and near real-time inference</li><li>Implement model monitoring, drift detection, and retraining workflows</li><li>Collaborate closely with data engineering and backend teams to productionize ML systems</li></ul><p><br /></p><h3><strong>Required Skills &amp; Experience</strong></h3><ul><li>6–8 years of hands-on experience in Machine Learning / Applied AI</li><li>Strong proficiency in Python, SQL, and FastAPI</li><li>Solid understanding of time-series forecasting techniques</li><li>Experience with classification, regression, and ranking models</li><li>Practical experience with tree-based models (XGBoost, LightGBM, Random Forest)</li><li>Experience designing production ML pipelines</li><li>Strong understanding of feature engineering, model evaluation, and explainability</li></ul><p><br /></p><h3><strong>Good to Have</strong></h3><ul><li>Experience with spatio-temporal models (ST-GNNs, Transformers, temporal CNNs)</li><li>Exposure to Reinforcement Learning, contextual bandits, or MDP-based systems</li><li>Experience with graph-based modeling</li><li>Familiarity with MLflow, feature stores, Kubernetes</li><li>Experience working with large-scale, high-frequency data systems Python, Deep Learning, Neural Networks, AI, Time Series, ML Algorithms</li></ul><h2><br /></h2>