Lead the advancement of utility intelligence systems at Themis Intelligence as a Machine Learning Engineer. This hybrid role involves developing ML models for geospatial and time-series data.
As a pivotal member of the team at Themis Intelligence, you'll work on cutting-edge machine learning systems that support critical grid settings. This is a hybrid position focused on implementing production-grade pipelines, deploying models, and ensuring reliability through mature MLOps practices. Key areas of expertise include statistical methods and large-scale data handling, making your contributions crucial to our mission.
Key Responsibilities:
• Develop machine learning models for forecasting and anomaly detection • Design ML system architecture for scalability and reproducibility • Build end-to-end MLOps pipelines for data ingestion and training • Deploy models reliably across cloud-native and on-premises infrastructure • Ensure all models are continuously monitored and evaluated
Requirements: • Bachelor's degree in a relevant technical field • 3+ years of experience in machine learning or applied AI • Solid skills in time-series modeling and deep learning • Experience with geospatial data and high-dimensional datasets • Familiarity with Python-based ML tools like PyTorch
Contribute to the future of utility operations by applying your machine learning expertise at Themis Intelligence. #J-18808-Ljbffr