Machine Learning Engineer II

About this role

At Niagara, we’re looking for Team Members who want to be part of achieving our mission to provide our customers the highest quality most affordable bottled water.

Consider applying here, if you want to:    • Work in an entrepreneurial and dynamic environment with a chance to make an impact.    • Develop lasting relationships with great people.    • Have the opportunity to build a satisfying career.

We offer competitive compensation and benefits packages for our Team Members.

Machine Learning Engineer II

As a Machine Learning Engineer II, you will design, build next-generation predictive and prescriptive maintenance systems utilizing Azure Machine Learning Studio from end to end. Drawing on industrial sensor data and machine PLC , you will develop cutting-edge models that detect failure signatures before they occur and prescribe optimized corrective actions. You will own the end-to-end industrial ML lifecycle. You will design, train, and optimize supervised and unsupervised architectures to accurately predict equipment Remaining Useful Life (RUL), detect complex anomalies, and deploy prescriptive Agentic AI decision workflows. Once validated, you will deploy these models to low-latency cloud and edge endpoints, seamlessly integrating predictions with plant dashboards, End points Applications and CMMS workflows. Finally, you will establish automated MLOps pipelines in Azure ML Studio to continuously monitor data drift and trigger zero-downtime model retraining as physical factory environments evolve.

This is a high-impact & cross-functional engineering role requiring strong technical depth, system-level thinking, and the ability to communicate complex solutions effectively to diverse audiences including operations (Manufacturing plants), IT, systems Engineering, and executive leadership.

Key Responsibilities 1. Advanced ML Modeling & Algorithmic • Supervised & Unsupervised Learning: Build robust classifiers for fault diagnosis and regression models for Remaining Useful Life (RUL) estimation. Expertly handle highly imbalanced datasets where failure labels are rare. • Agentic AI & Prescriptive Systems: Develop multi-agent workflows that reason over asset health data, parse digital manuals via RAG (Retrieval-Augmented Generation), interact with operational APIs, and generate automated outputs. • Utilizing XGBoost, Random Forests, LSTMs, and Autoencoders—to process sensor streams and PLC data for predictive maintenance and real-time anomaly detection. leverage techniques like Isolation Forests, One-Class SVMs, Dynamic Time Warping, and PCA to build scalable models that monitor asset health, classify process quality, and drive automated decision-making.

2. Production-Grade MLOps & Infrastructure • Robust Data Engineering: Standardize, clean, and enrich raw, unstructured, or missing sensor telemetry and PLC tag data. • Scalable ML Pipelines: Build and maintain scalable, reproducible training and inference pipelines (using MLflow, Kubeflow, or Azure Machine Learning). • Edge & Cloud Deployment: Deploy models across hybrid environments, optimizing for cloud (Azure) as well as low-latency. • Distributed Compute Tuning: Optimize model training and throughput, leveraging GPU-accelerated training and efficient serialization for massive datasets.

3. Systems Integration & Cross-Functional Impact • High-Fidelity Code: Deliver highly optimized, production-grade, modular software in Python and C++ accompanied by strict unit testing, and clean documentation. • Technical Communication: Bridge the gap between data science and physical operations. Clearly articulate complex ML mechanics, decision boundaries, and model limitations to plant managers, IT directors, and executive leadership.

Qualifications & Deep Technical Requirements Technical Skills (Must-Haves): • 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++ & 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.

• This position is estimated to travel 10-30% • Please note this job description is not a full list of activities, duties or responsibilities required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without prior notice.

This position embodies the values of Niagara’s LIFE competency model, focusing on the following key drivers of success: 

• Lead Like an Owner 

• Manages a safe working environment, accurately documents safety-related training, and effectively communicates safety incidents  • Provides strategic input and oversight to departmental projects  • Makes data-driven decisions and develops sustainable solutions  • Skilled in reducing costs and managing timelines while prioritizing long-run impact over short-term wins  • Makes decisions by putting overall company success first before department/individual success  • Leads/facilitates discussions to get positive outcomes for the customer  • Makes strategic decisions that prioritize the needs of the customer over departmental/individual goals 

• InnovACT 

• Continuously evaluates existing programs and processes, and develops new initiatives to increase efficiency and reduce waste  • Creates, monitors, and responds to departmental performance metrics to drive continuous improvement  • Champions responsible adoption of Agentic AI and intelligent automation to improve reliability, speed, decision quality, and waste reduction while maintaining safety and governance.  • Communicates a clear vision, organizes resources effectively, and ad

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