Machine Learning Engineer
Core
Develop and deploy machine learning and deep learning models for time-series forecasting, anomaly detection, and geospatial intelligence to support critical grid environments.
Role type
Machine Learning Engineer (Applied AI)
Builds
Production-grade ML systems, MLOps pipelines, and data systems for utility operations
Domain
Energy/Utilities, Time-series analysis, Geospatial intelligence
Deliverable
production ML models
Required skills
Time-series forecasting, Anomaly detection, Geospatial intelligence, MLOps pipeline design, Data ingestion, Model registry management, CI/CD integration, Model monitoring, Dimensionality reduction (PCA), Feature engineering, Statistical validation, Foundation model application, Code review participation
Preferred skills
Experience with incomplete/noisy datasets, Cloud-native and on-premises deployment, Benchmarking framework design
Technologies
Cloud-native environments, On-premises infrastructure, MLOps tools
Responsibilities
Develop and deploy ML models for forecasting and detection, Design ML system architecture, Build and maintain end-to-end MLOps pipelines, Deploy models across cloud and on-premises, Work with large-scale noisy datasets, Design benchmarking frameworks, Ensure model observability and versioning, Write clean and documented code
Seniority
Mid-level, hands-on IC