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.

• Build time-series demand forecasting models using rolling statistics, classical ML, and deep learning
  
  

• Develop spatial / geospatial models using hexagonal or grid-based representations (e.g., H3)
  
  

• Implement classification models (logistic regression, tree-based models) for risk and state detection
  
  

• Design and optimize scoring and ranking engines using weighted heuristics and learned value functions
  
  

• Work on policy learning and reinforcement learning for long-horizon optimization
  
  

• Own feature engineering, training pipelines, and model evaluation
  
  

• Deploy models for batch and near real-time inference
  
  

• Implement model monitoring, drift detection, and retraining workflows
  
  

• Collaborate closely with data engineering and backend teams to productionize ML systems
  
  

• 5–7 years of hands-on experience in Machine Learning / Applied AI
  
  

• Strong proficiency in Python, SQL, and FastAPI
  
  

• Solid understanding of time-series forecasting techniques
  
  

• Experience with classification, regression, and ranking models
  
  

• Practical experience with tree-based models (XGBoost, LightGBM, Random Forest)
  
  

• Experience designing production ML pipelines
  
  

• Strong understanding of feature engineering, model evaluation, and explainability
  
  

Good to Have

• Experience with spatio-temporal models (ST-GNNs, Transformers, temporal CNNs)
  
  

• Exposure to Reinforcement Learning, contextual bandits, or MDP-based systems
  
  

• Experience with graph-based modeling
  
  

• Familiarity with MLflow, feature stores, Kubernetes