Senior ML Engineer
Role Summary
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 ML and reinforcement learning, and deploying them at production scale.
Key Responsibilities
- 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 (e.g., 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
Required Skills & Experience
- 7–8 years of hands-on experience in Machine Learning / Applied AI
- Strong proficiency in Python and SQL, 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
- Experience working with large-scale, high-frequency data systems
