By combining state-of-the-art data science techniques, cutting-edge Internet of Things (IoT) technologies, and Software as a Service, we enable a more connected, intelligent and effective supply chain. We’re creating value from massive, connected data. Our unmatched insights illuminate more than 300,000 supply chains, more than a million customers and partners, and over 300 million physical assets that are constantly on the move around the world.CHEP is a Brambles / BXB Digital company, the global leader in supply chain logistic solutions operating through the CHEP brand. Brambles Limited is listed on the Australian Securities Exchange (ASX) and has its headquarters in Sydney, Australia. Operating in more than 60 countries, with its largest operations in North America and Western Europe, we employ more than 14,500 people and owns over 550 million pallets, crates and containers through a network of approximately 850 service centres.SENIOR MACHINE LEARNING ENGINEER POSITION PURPOSE We are seeking a Senior Machine Learning Engineer to design, build, deploy, and operate scalable machine learning and AI solutions in production. This role sits at the intersection of MLOps, traditional data science modeling, and software engineering, with opportunities to work on AI/GenAI engineering use cases.You will work closely with Data Scientists and Engineers to productionize ML and emerging GenAI solutions, owning the full lifecycle from model development through deployment, monitoring, and iteration.SCOPEMachine Learning models for Advanced D&A Americas.Data products initiatives for Advanced D&A Americas.GenAI initiatives for Advanced D&A Americas.MAJOR / KEY ACCOUNTABILITIESBuild, maintain, and optimize end to end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, inference and monitoring using Databricks and related tooling.Collaborate closely with Data Scientists to translate experimental and research grade models into reliable, scalable,



and secure production services that meet business and technical requirements.Apply MLOps best practices including model versioning, experiment tracking, monitoring, and automated deployments.Develop and deploy traditional ML models (e.G., regression, classification, forecasting, NLP) to solve business problems.Implement runtime monitoring dashboards and alerting mechanisms to detect performance degradation, data anomalies, and system failures in near real time.Support AI / GenAI initiatives, including LLM based prototypes and production workflows where applicable.Collaborate with product owners, data scientists, engineers, and business stakeholders to define model requirements, SLAs, success metrics, and deployment constraints.Integrate ML solutions into downstream systems via APIs, batch pipelines, or event driven processes.Write high quality, maintainable code following engineering best practices, with version control and CI/CD in Bitbucket.Troubleshoot and optimize model performance, scalability, latency, and cost in production environments.Provide guidance and best practices to data scientists and engineers on production ready ML development and MLOps workflows.Evaluate emerging tools, frameworks, and practices to enhance the organization’s ML and GenAI operational maturity.MEASURESML models are reliable, scalable, and observable in production environmentsReduced time and friction moving from experimentation to production ML systemsHigh availability and reliability of ML pipelines and inference servicesStrong collaboration with Data and cross functional teams resulting in business impacting ML solutionsClear observability into model performance, data quality,



and system healthAdoption of standardized patterns for ML development and deployment across the teamKEY CONTACTS QUALIFICATIONSBachelor’s or master’s degree in computer science, Engineering, Data Science, Mathematics, or a related field, or 7+ years of equivalent professional experience in a related roleStrong foundation in machine learning algorithms and applied modeling techniquesDemonstrated ability to build and operate production grade software systems is a plusProven ability to work in ambiguous problem spaces and evolving AI landscapesEXPERIENCE5+ years of experience in Machine Learning Engineering, Applied Machine Learning, or a closely related roleHands on experience deploying and supporting ML models in productionProven experience using ML lifecycle management tools such as MLflow (preferred) or similar platformsExperience using Databricks or similar platforms for data processing and ML workloadsProven collaboration with Data Scientists and Engineers in cross functional teamsExperience supporting both early stage experimentation and production systemsSKILLS AND KNOWLEDGEStrong understanding of supervised and unsupervised learning techniquesFeature engineering, model evaluation, and performance optimizationExperience operationalizing models beyond notebooksBuilding and maintaining ML pipelines (training, inference, retraining)Model versioning, experiment tracking, and reproducibilityMonitoring for model performance, data drift, and pipeline failuresCI/CD practices for ML workflowsStrong proficiency in PythonGit based version control workflowsExperience integrating ML into applications or servicesExposure to LLMs, embeddings, prompt engineering, or retrieval augmented generation (RAG)Experience moving GenAI use cases from prototype to productionFamiliarity with evaluating GenAI outputs and monitoring cost, latency, and qualityExperience building or consuming REST APIs for model inferenceUnderstanding of distributed systems and data pipelinesThe salary range for this position is $103,000 to $140,000 / year. Salary ranges provided take into

📌 Senior Machine Learning Engineer - Mississauga, On (Hybrid) - $103,000 - $140,000 A Year
🏢 CHEP
📍 Mississauga