Senior Machine Learning Engineer

webAI; Toronto; Hybrid; CAD 110,000 - 150,000; Full time

Job summary

A leading tech company in Toronto seeks a Senior Machine Learning Engineer to develop ML infrastructure and lead analytics for Fortune 500 companies. The role emphasizes the design of scalable ML systems, generative AI integration, and strategic communication with leadership. This hybrid position offers competitive pay and extensive growth opportunities.

Benefits

Competitive salary

Strong healthcare insurance

Extensive learning and development resources

Qualifications

• 5+ years of ML engineering experience with Fortune 500 companies.
• Expert-level in MLflow and Databricks ML platform.
• Strong background in supervised/unsupervised learning and deep learning.

Responsibilities

• Design and implement MLOps infrastructure using MLflow and Databricks.
• Build ML model deployment pipelines and assess AI readiness.
• Provide strategic guidance for implementing AI-driven analytics systems.

Skills

MLflow

Databricks

Machine Learning Algorithms

Generative AI

Real-time ML Inference

Strategic Communication

Education

Bachelor's or Master's degree in Computer Science or related field

Job description

Position Overview:

ShyftLabs is seeking an experienced Senior Machine Learning Engineer to design and implement ML infrastructure and assess Agentic BI readiness for Fortune 500 enterprise companies. You will build robust MLOps platforms, design scalable ML pipelines, and provide strategic guidance for implementing autonomous business intelligence and AI-driven analytics systems.

ShyftLabs is a growing data product company founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation.

Job Responsibilities:

• Design and implement MLOps infrastructure using MLflow, Databricks Unity Catalogue, and AWS managed services
• Build feature store implementations and ML model versioning strategies using Databricks and MLflow
• Assess AI readiness and design roadmaps for Agentic BI implementations supporting autonomous insights generation
• Design production ML systems supporting predictive analytics, classification, and optimization models
• Implement ML model deployment pipelines with automated training, validation, and deployment workflows
• Build model monitoring and performance management systems for production ML applications
• Evaluate generative AI infrastructure requirements including semantic layers and automated analytics workflows
• Design ML pipeline automation strategies integrating feature engineering, model training, and deployment processes
• Implement real-time ML inference patterns supporting business-critical applications
• Enterprise MLOps Expertise: Proven experience implementing ML infrastructure at Fortune 500 scale
• Agentic BI Assessment: Understanding of autonomous AI systems and ability to assess organizational readiness for AI-driven business intelligence
• Production ML Focus: Deep understanding of ML model deployment, monitoring, and lifecycle management in production environments
• Strategic Communication: Strong consulting skills to present ML strategies and AI readiness roadmaps to executive leadership

Basic Qualifications:

• Bachelor's or Master's degree in Computer Science, Machine Learning, Engineering, or related quantitative field
• 5+ years of experience in ML engineering with Fortune 500 enterprise-scale implementations
• Expert-level experience with MLflow for model lifecycle management and experimentation tracking
• Deep hands-on experience with Databricks ML platform including Unity Catalogue for ML governance
• Proven experience with AWS ML services including SageMaker, model deployment, and managed ML infrastructure
• Strong background in machine learning algorithms including supervised/unsupervised learning, ensemble methods, and deep learning
• Experience with generative AI and LLM integration for business intelligence applications and semantic data modeling requirements
• Knowledge of feature store architectures, ML data management patterns, and model versioning/automation workflows

Preferred Qualifications

• Experience with Agentic BI frameworks and autonomous analytics systems
• Knowledge of conversational AI and natural language interfaces for business intelligence
• Understanding of AI governance frameworks and enterprise AI readiness assessment
• Experience with real-time recommendation systems and live inference pipelines
• Familiarity with financial modeling or pricing optimization ML applications
• Understanding of A/B testing frameworks for ML model evaluation
• Knowledge of ML governance and regulatory compliance requirements

We are proud to offer a competitive salary alongside a strong healthcare insurance and benefits package. The role is preferably hybrid, with 2 days per week spent in the office, and flexibility for client engagement needs. We pride ourselves on the growth of our employees, offering extensive learning and development resources.

ShyftLabs is an equal-opportunity employer committed to creating a safe, diverse and inclusive environment. We encourage qualified applicants of all backgrounds including ethnicity, religion, disability status, gender identity, sexual orientation, family status, age, nationality, and education levels to apply. If you are contacted for an interview and require accommodation during the interviewing process, please let us know.