Machine Learning Engineer II

About the role

As a Machine Learning Engineer II at Niagara, you will play a crucial role in developing predictive and prescriptive maintenance systems using Azure Machine Learning Studio. Your work will involve designing, building, and deploying models that can detect failures before they occur and provide optimized corrective actions.

Responsibilities

• Design, build, and deploy next-generation predictive and prescriptive maintenance systems utilizing Azure Machine Learning Studio from end to end.
• Develop cutting-edge models that detect failure signatures before they occur and prescribe optimized corrective actions.
• Own the end-to-end industrial ML lifecycle, including designing, training, and optimizing supervised and unsupervised architectures.
• Process sensor streams and PLC data for predictive maintenance and real-time anomaly detection using techniques like Isolation Forests, One-Class SVMs, Dynamic Time Warping, and PCA.
• Deploy models to low-latency cloud and edge endpoints, integrating predictions with plant dashboards, applications, and CMMS workflows.
• Establish automated MLOps pipelines in Azure ML Studio to continuously monitor data drift and trigger zero-downtime model retraining as physical factory environments evolve.
• Bridge the gap between data science and physical operations by clearly articulating complex ML mechanics, decision boundaries, and model limitations to plant managers, IT directors, and executive leadership.

Qualifications

• Advanced ML Modeling & Algorithmic: Build robust classifiers for fault diagnosis and regression models for Remaining Useful Life (RUL) estimation. Handle highly imbalanced datasets.
• Agentic AI & Prescriptive Systems: Develop multi-agent workflows that reason over asset health data, parse digital manuals via RAG, interact with operational APIs, and generate automated outputs.
• Frameworks & Libraries: Deep expertise in PyTorch or TensorFlow, alongside standard data science libraries (Scikit-Learn, NumPy, Pandas, SciPy).
• Production Programming: Exceptional software development skills in Python (writing optimized, vectorized code), Java Script, C/C++, and R.
• Modern MLOps & Cloud: Hands-on experience with containerization (Docker/Kubernetes), distributed processing (PySpark/Databricks), and cloud architectures, ideally Microsoft Azure.
• Data Handling: Mastery of SQL, NoSQL, and time-series databases (e.g., InfluxDB, TimescaleDB) containing millions of streaming data points.

Skills

• Framework & Libraries: Deep expertise in PyTorch or TensorFlow, alongside standard data science libraries (Scikit-Learn, NumPy, Pandas, SciPy).
• Production Programming: Exceptional software development skills in Python (writing optimized, vectorized code), Java Script, C/C++, and R.
• Modern MLOps & Cloud: Hands-on experience with containerization (Docker/Kubernetes), distributed processing (PySpark/Databricks), and cloud architectures, ideally Microsoft Azure.
• Data Handling: Mastery of SQL, NoSQL, and time-series databases (e.g., InfluxDB, TimescaleDB) containing millions of streaming data points.

Benefits

Our Total Rewards package includes a comprehensive benefits program for regular full-time team members, including paid time off, parental leave, and vacation time. Part-time, intern, and seasonal team members receive a limited benefits package.