CA$94,552 – CA$118KMidFull Time
Come join Pattison Food Group Ltd., a Jim Pattison business, and Canada’s largest Western-based provider of food and health products.
Pattison Food Group Ltd. consists of Save-On-Foods and other well-known grocery banners. With nearly 300 retail locations, we proudly employ more than 30,000 team members.
We have an exciting opportunity for a full-time Machine Learning Engineer to join our IT team in Langley, BC.
Reporting to the Manager, Data & Analytics you will work closely with the Data Scientists and Data Engineers to help extract value from our data by leading all the processes from data collection, cleaning, and preprocessing, to training models and deploying them to production.
You will be responsible for:
Building and integrating end to end lifecycles of large-scale, distributed machine learning systems using the latest open source and cloud technologies.
Maintaining cloud workspaces and optimizing cloud compute resources.
Building automated tests and validations for machine learning models and underlying data.
Maintaining production machine learning model registry and building retraining strategies.
Developing and implementing methods for detecting model and data drifts.
Developing scalable tools and services for handling machine learning workflows.
Implementing cloud distributed approaches for deep learning models.
Collaborating with data scientists and data engineers to test and deploy ML models at scale.
Identifying and testing the latest technological tools that can help improve the performance and maintenance of our machine learning systems.
Identifying project requirements, tracking, and communicating project progress with peers, and driving projects to completion.
Sharing knowledge through documentation and presentations to peers and senior leaders.
Dealing with any unexpected pipeline production issues that might require an MLE intervention.
You have:
A Master's or bachelor's degree in computer science, applied mathematics, or engineering.
3+ years of experience in building end-to-end machine learning pipelines.
Experience in MLOps to deploy and maintain machine learning models.
Experience working with Large Language Models (LLMs) including prompt engineering, fine-tuning and integrating LLM-based solutions into production-grade machine learning systems (nice to have).
Experience building, managing, and deploying Databricks Asset Bundles (DABs) or similar infrastructure-as-code frameworks to package, version, and promote data and ML workloads across environments (nice to have).
Experience building systems with scalable data processing technologies using Spark, Python, SQL.
Experience in CI/CD and be able to follow standard workspace branching practices.
Exposure to Databricks and associated tools like ML Flow and Delta Lake.
Familiarity with data-oriented workflow orchestration frameworks (Databricks Jobs, Azure Data Factory, Airflow etc.)
Experience developing containers in cloud computing environments (Azure, AWS, GCloud, etc.)
Exposure to machine learning and drift detection methodology and best practices.
Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, etc..)
Rigor in high code quality, automated testing, and other engineering best practices.
Experience working with cloud computing and database systems.
Fluency in one or more programming languages (Python, SQL, etc..)
Experience with version control tools such as Git and Git workflows.
You are:
Collaborative.
Self-motivated.
Innovative.
Able to work independently while part of a team.
A Critical and analytical thinking.
What we offer:
In addition to a highly competitive salary, we offer a great range of benefits, a company pension plan, free parking, exclusive team member offers, and opportunities for development and career progression.
If you’re looking to join a team that puts people first, you’re in the right place. Apply online today!
IND6
Compensation Details:
$94,552.00 - $118,190.00The compensation offered for this position will take into consideration location, education, skills, experience and other factors.
