Role Overview

We are seeking a talented and driven Machine Learning Engineer to design, build, and scale our next-generation AI and analytics platforms. In this role, you will bridge the gap between data science and production engineering, leveraging the Databricks ecosystem to deploy robust ML models, explore cutting-edge NLP/GenAI applications, and empower the business with self-service analytics. If you love optimizing workflows and turning complex data into intelligent, real-world solutions from your Canadian home office, we want to hear from you.

Key Responsibilities

End-to-End ML Development: Design, build, and deploy scalable machine learning solutions, NLP applications, and Generative AI (GenAI) frameworks.

Pipeline Engineering: Develop and manage production-grade ML pipelines using Databricks, Apache Spark, and MLflow for seamless model tracking and deployment.

Self-Service Analytics: Configure and optimize Databricks Genie to democratize data insights and enable automated, natural-language data discovery across teams.

Workflow Optimization: Maintain, monitor, and continuously improve existing production ML workflows, ensuring high availability, speed, and reliability.

Collaboration: Work closely with data scientists, data engineers,



and business stakeholders to translate complex requirements into robust data products.

Primary Skills (Mandatory) Programming & Querying: Advanced proficiency in Python and SQL for data manipulation and model development.

Machine Learning: Solid foundation in core ML algorithms, statistical modeling, and data science principles.

Databricks Ecosystem: Hands-on experience building and deploying models within Databricks, utilizing Apache Spark for distributed computing and MLflow for the ML lifecycle.

MLOps: Demonstrated experience in ML Ops practices, including model versioning, CI/CD pipelines for ML, automated testing, and production monitoring.

Secondary Skills (Preferred & Nice-to-Have) GenAI & NLP: Experience working with Large Language Models (LLMs), prompt engineering, or semantic search frameworks.

Advanced Analytics Configuration: Direct experience or strong familiarity with setting up Databricks Genie spaces.

Domain Expertise: Prior experience in the Retail industry or retail analytics (e.g., demand forecasting, customer churn, recommendation engines) is a significant plus.

Cloud Platforms: Familiarity with cloud infrastructure (AWS, Azure, or GCP) as it integrates with Databricks.

📌 Machine Learning Engineer (Toronto)
🏢 LatentView Analytics
📍 Toronto