Lead Machine Learning Engineer (AI)

TL;DR

Lead Machine Learning Engineer (AI/MLOps): Developing end-to-end scalable machine learning systems and applications with an accent on modern architectures and MLOps principles. Focus on designing technical solutions, building ML pipelines, and ensuring the responsible deployment of AI models in high-stakes projects.

Location: Toronto, Canada (Remote flexibility indicated)

Salary: $156,000 — $251,000 CAD

Company

A leading global technology consultancy specializing in solving complex business problems through innovative software and AI solutions.

What you will do

• Lead the design and development of scalable, maintainable ML systems and end-to-end applications.
• Oversee program inception, translating client needs into technically feasible ML solutions.
• Own the full ML lifecycle, including pipelines, model training, deployment, monitoring, and evaluation.
• Champion Responsible AI practices and promote technical excellence within the engineering team.
• Mentor and coach teammates, fostering a collaborative environment through hands-on coding and guidance.
• Align technical strategies with organizational goals to deliver tangible value to clients.

Requirements

• Proven experience in developing technical visions and strategies aligned with business needs.
• Proficiency in writing clean, maintainable, and testable code using Python.
• Experience with distributed systems and scalable architectures for large-scale ML applications.
• Hands-on expertise with ML frameworks: Scikit-learn, TensorFlow, PyTorch, MLFlow, or Kubeflow.
• Experience implementing MLOps principles and CI/CD for machine learning.
• Proficiency with cloud platforms such as Azure, AWS, GCP, or Databricks.

Culture & Benefits

• Cultivation culture focusing on autonomy and personalized career development.
• Access to numerous development programs and interactive growth tools.
• Inclusive community of supportive colleagues pushing technology boundaries.
• Strong emphasis on mentorship, knowledge sharing, and continuous learning.