Senior Machine Learning Engineer

Work location and hours

• Work location listed as Toronto, Ontario, Canada.
• Hours are 37.5; the posting does not specify a compensation cadence.

Pay details

• Posted base pay range is $115,600–$163,200 CAD.
• The role is temporarily eligible for a pay premium above the posted range, reassessed annually; actual pay depends on skills, experience, location, and business needs.

Role overview

Key accountabilities

• Build end-to-end generative AI solutions with Azure AI and Google AI services; lead production LLM applications using RAG, prompt engineering, fine-tuning, and multi-agent architectures.
• Implement AI security protections and deliver content filtering, output monitoring, and safety guardrails.
• Architect MLOps and AIOps for versioning, retraining, monitoring, explainability, and 99.9% availability of mission-critical services.
• Turn business objectives into AI/ML proofs of concept and production solutions; demonstrate ROI through rapid prototyping and iteration.
• Drive AI/ML technical strategy across hybrid cloud, evaluating small language models, agentic AI, and edge deployment.
• Mentor platform engineers and data scientists in ML, LLMOps, and responsible AI.
• Evaluate AI vendors and tools and recommend strategic technology investments.

Experience and education

• Bachelor's degree in Computer Science, Machine Learning, Mathematics, or a related technical field.
• 5+ years of hands-on AI/ML engineering, including 3+ years focused on generative AI and LLM applications.
• 5+ years of executive leadership experience in financial services and ability to influence C-suite stakeholders.
• Expert Python proficiency, ML frameworks, and LLM orchestration frameworks.
• Hands-on enterprise deployment experience with Azure OpenAI, Azure ML Studio, Databricks MLflow, and Google Vertex AI.
• MLOps/LLMOps, ML CI/CD, infrastructure-as-code, and containerization experience.
• Experience with real-time inference, streaming, edge deployment, multimodal models, computer vision, and advanced NLP.

Technical environment

• Technologies named include Azure AI, Google AI services, PyTorch or TensorFlow, LangChain or LlamaIndex, Azure DevOps or GitHub Actions, Terraform, Docker, Kubernetes, Kafka, and Pub/Sub.