Job Description

Job Description

Job Description

We’re currently seeking a seasoned Machine Learning Engineer who thrives at the intersection of data science, engineering, and operations. This role is ideal for someone who has not only mastered the craft of building and scaling ML solutions, but also knows how to put them into production, and keep them running smoothly.

What You’ll Be Doing:

The role is a mix of MLOps, platform development, and hands-on model engineering. You’ll be responsible for designing automated workflows that take ML models from development to deployment with minimal friction. This includes developing robust CI/CD pipelines (preferably using Azure DevOps or GitHub Actions) and managing model lifecycle tasks such as versioning, monitoring, and retraining.

A large portion of your time will be spent working within Databricks—developing feature pipelines, optimizing model workflows, and integrating ML components with existing SaaS systems. You’ll also help migrate and scale models within an environment already hosting over 100 ML models, with another 25+ targeted for transition.

On the application side, you’ll work closely with data scientists to productionalize machine learning prototypes. You’ll select appropriate algorithms, build scalable ML systems using Python-based frameworks like TensorFlow and PyTorch, and help tune and evaluate performance over time.

The Ideal Candidate Brings:

• 5+ years of experience in software, data, or DevOps engineering roles—with at least 3 of those focused on MLOps or ML engineering.
• Expertise in Python, and confidence working with key ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Hands-on experience working in the Microsoft Azure ecosystem—particularly Azure ML, AKS, and Azure DevOps.
• Deep familiarity with Databricks (including Delta Lake, MLflow, and Apache Spark) is non-negotiable.
• An understanding of infrastructure-as-code (e.g., Terraform), version control, and CI/CD practices in ML workflows.
• Strong communication skills and the ability to work cross-functionally across technical and business teams.
• You must be located in Canada and able to work with Canadian teams.

Additional:

• Certifications from Databricks or Microsoft (e.g., Azure Solution Architect).
• Experience working with LLM libraries or frameworks like transformers, trl, or deepspeed.
• Prior involvement in ML model migration projects or SaaS ML platform integration.
• A consulting background or client-facing role where clear communication and stakeholder management were key.

PandoLogic. Keywords: Machine Learning Engineer, Location: Toronto, ON - M5R 1M4

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