Machine Learning Engineer II

Niagara Bottling; Diamond Bar (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Niagara Bottling seeks a Machine Learning Engineer II in California to design and own predictive and prescriptive maintenance systems, leveraging industrial sensor data and Azure Machine Learning Studio.

The role develops advanced models (RUL, anomaly detection, NLP) and builds robust ML pipelines, deploying to hybrid cloud and edge with GPU acceleration while communicating insights to plant leadership.

Qualifications

• BS in CS, data science, or related quantitative field.
• 5–7 years in Python, R, or another programming language.
• 5–7 years with TensorFlow, PyTorch, or scikit-learn.
• 3–5 years in NLP (LLM, RAG) and agentic AI.
• 3–5 years in responsible AI and security considerations.
• Experience with Docker, Kubernetes and ML ops.
• Travel 10–30%.
• This role is open to Los Angeles County residents only.
• Strong communication and analytical skills.

Responsibilities

• Design, build and own end-to-end predictive and prescriptive maintenance systems using Azure Machine Learning Studio, leveraging industrial sensor and PLC data.
• Develop advanced supervised and unsupervised models to predict Remaining Useful Life (RUL), detect complex anomalies, and prescribe optimized corrective actions.
• Create agentic AI and prescriptive workflows that reason over asset-health data, parse digital manuals via Retrieval-Augmented Generation, interact with operational APIs, and generate automated outputs.
• Standardize, clean, enrich raw telemetry and PLC tag data, and build scalable, reproducible training and inference pipelines (MLflow, Kubeflow, Azure ML).
• Deploy models to hybrid cloud and edge endpoints with low latency.
• Optimize distributed training and inference, leveraging GPU acceleration and efficient serialization for massive datasets.
• Deliver high-fidelity, production-grade code in Python and C++, accompanied by unit testing and documentation.
• Communicate complex ML mechanics and model limitations to plant managers, IT directors, and executive leadership, bridging data science and physical operations.
• Establish automated MLOps pipelines that continuously monitor data drift and trigger zero-downtime model retraining as factory environments evolve.

Skills

Python

R programming

TensorFlow

PyTorch

scikit-learn

NLP

Agentic AI

Responsible AI

SQL

NoSQL time-series

Education

Bachelor’s degree in CS, Data Science, or related

Master’s or PhD with ML focus

Tools

Docker

Kubernetes

MLflow

Azure Cloud

CI/CD for ML

TimescaleDB

InfluxDB

Job description

Machine Learning Engineer II – Niagara Bottling

We’re looking for Team Members who want to be part of achieving our mission to provide customers the highest quality and most affordable bottled water. Join an entrepreneurial and dynamic environment where you can make an impact, develop lasting relationships, and build a satisfying career.

Key Responsibilities

• Design, build and own end‑to‑end predictive and prescriptive maintenance systems using Azure Machine Learning Studio, leveraging industrial sensor and PLC data.
• Develop advanced supervised and unsupervised models to predict Remaining Useful Life (RUL), detect complex anomalies, and prescribe optimized corrective actions. Handle highly imbalanced datasets and build robust classifiers and regression models.
• Create agentic AI and prescriptive workflows that reason over asset‑health data, parse digital manuals via Retrieval‑Augmented Generation, interact with operational APIs, and generate automated outputs. Use XGBoost, Random Forest, LSTM, Autoencoder, Isolation Forest, One‑Class SVM, Dynamic Time Warping, and PCA.
• Standardize, clean, enrich raw telemetry and PLC tag data, and build scalable, reproducible training and inference pipelines (MLflow, Kubeflow, Azure ML). Deploy models to hybrid cloud and edge endpoints with low latency.
• Optimize distributed training and inference, leveraging GPU acceleration and efficient serialization for massive datasets.
• Deliver high‑fidelity, production‑grade code in Python and C++, accompanied by unit testing and documentation.
• Communicate complex ML mechanics and model limitations to plant managers, IT directors, and executive leadership, bridging data science and physical operations.
• Establish automated MLOps pipelines that continuously monitor data drift and trigger zero‑downtime model retraining as factory environments evolve.

Qualifications

• Education: Bachelor’s degree in Computer Science, Data Science, Electrical/Mechanical Engineering, Mathematics, or a related quantitative field. Master’s or PhD with a machine‑learning focus is highly preferred.
• Experience: 5–7 years in Python, R, or another programming language; 5–7 years with TensorFlow, PyTorch, or scikit‑learn; 5–7 years in Industrial ML, Automation, or related fields; 3–5 years in NLP (LLM, RAG) and agentic AI; 3–5 years in responsible AI and security considerations.
• Technical Skills: Deep expertise in PyTorch or TensorFlow; proficient in Python, JavaScript, C/C++, and R; experience with Docker, Kubernetes, CI/CD for ML, MLflow, and Azure Cloud; mastery of SQL and NoSQL time‑series databases (InfluxDB, TimescaleDB).
• Travel: Estimated 10‑30%.
• Additional Requirements: This position is open to LosAngelesCounty residents only. Qualified applicants with arrest or conviction records will be considered in accordance with the LosAngelesCounty Fair Chance Ordinance and the California Fair Chance Act.
• Strong communication, problem‑solving, and analytical skills; ability to work with minimal supervision and in a dynamic environment.

Equal Opportunity Statement

NiagaraBottlingLLC is an Equal Opportunity Employer that does not discriminate on the basis of race, color, religion, sex, age, sexual orientation, gender identity and/or expression, national origin, veteran status or disability in relation to recruiting, hiring and promotion practices.