Machine Learning Engineer – Industrial Process Environment Work across the full project lifecycle: scoping problems with plant engineers, wrangling messy sensor data, building and deploying models, and ensuring they work in production for fault detection, predictive maintenance, quality optimization, and process control. Reports To Operations Director Responsibilities Develop and deploy ML models (classification, regression, anomaly detection, time‑series forecasting) for industrial process applications. Collaborate with process engineers and operators to translate domain problems into well‑scoped ML tasks. Build robust data pipelines from historians, SCADA systems, and other industrial data sources. Design feature engineering strategies grounded in physical process understanding. Validate models against real plant conditions, not just offline metrics. Containerize and deploy models using Docker, with experience in Kubernetes or similar orchestration tools. Support model monitoring, retraining workflows, and CI/CD for ML pipelines. Travel domestically and internationally as required. Qualifications Degree in Engineering (Electrical, Mechanical, Chemical, or similar), Computer Science, or related scientific/technical field. 3‑5 years of experience in applied ML or data science, ideally in manufacturing, process industries, or adjacent fields. Strong Python skills: scikit‑learn, pandas, NumPy. Experience with a range of ML approaches: gradient boosting (LightGBM, XGBoost), deep learning frameworks (PyTorch or TensorFlow), and unsupervised methods. Familiarity with time‑series data and its challenges (irregular sampling, sensor drift, missing data, class imbalance). Understanding of process engineering fundamentals: heat/mass balance, process flow diagrams, and common unit operations. Proficiency with Docker; experience with Kubernetes, Helm, or cloud container services. Ability to communicate model results and limitations clearly to non‑ML stakeholders. Must be eligible to work in the United States and Canada or able to obtain appropriate work authorization. Pay Range 120k to 180k CAD. Highly Valued Experience Experience with process control systems (DCS/PLC), control loop tuning, SCADA, and MES systems. Familiarity with OPC‑UA, MQTT, PI Historian, or similar industrial data infrastructure. Exposure to Bayesian methods or probabilistic modeling. Experience with MLOps tooling (MLflow, Kubeflow, Airflow, or similar). Experience deploying models in edge, on‑premise, and cloud environments. Background in controls or process engineering. #J-18808-Ljbffr