ML Engineer- Senior or Mid level

Processing thousands of energy data points per second from diverse operational sources, handling massive volumes of energy data while running sophisticated classification and anomaly detection models in real-time, maintaining comprehensive data lineage, and delivering insights through high-performance platforms used by energy operators globally requires exceptional engineering and scientific expertise. This processing demands models that can withstand the scrutiny of energy analysts and traders, operations teams, and regulatory bodies, with the performance, stability, and reliability that critical energy systems require.

The Data Platform Team is responsible for all machine learning operations across our energy data ecosystem. We work with everything from raw sensor data from millions of energy assets to complex operational datasets, generating high-value predictions such as equipment failure detection, energy demand forecasting, operational anomaly identification, predictive maintenance scheduling, and system optimization recommendations.

The team has built a comprehensive suite of statistical and machine learning models that enable us to provide the most accurate and actionable insights into energy operations. We take pride in applying cutting-edge research to real-world energy challenges in a robust, scalable, and maintainable way. The quality of our models is continuously validated by experienced in-house energy analysts and traders and domain experts to ensure reliability of our predictions.

You'll be instrumental in designing and building ML infrastructure and applications to propel the design, deployment, and monitoring of existing and new ML pipelines and models. Working with software engineers, data scientists, and energy analysts and traders, you'll help bridge the gap between research experiments and production energy systems by ensuring 100% uptime and bulletproof fault-tolerance of every component of our ML platform.

Requirements

You Are :

• Experienced in building and deploying distributed scalable ML pipelines that can process large volumes of energy data daily using Kubernetes and MLflow
• With solid machine learning engineering fundamentals, fluent in Python, PyTorch, and XGBoost Skilled in developing classification models and anomaly detection systems for production environments Capable of implementing comprehensive data lineage tracking and model governance systems
• Driven by working in an intellectually engaging environment with top energy analysts and traders and technology experts, where constructive challenges and technical debates are encouraged
• Excited about working in a dynamic environment : not afraid of complex energy challenges, eager to bring new ML innovations to production, and a positive can-do attitude
• Passionate about mentoring team members, helping them improve their ML engineering skills and grow their careers
• Experienced with the full ML model lifecycle, including experiment design, model development, validation, deployment, monitoring, and maintenance

Awesome If You :

Benefits