Manager, ML/AI Engineer, Data & AI

Job description

Toast is recruiting on behalf of one of Canada’s largest professional services firms, a global Big Four organization with thousands of professionals across the country helping Canada’s biggest companies solve their most important challenges. Their Digital practice is building out its Data and AI team and is looking for a Manager, ML/AI Engineer who loves getting their hands dirty and working directly with clients. You’ll take machine learning and generative AI use cases from idea to secure, production-ready solutions across cloud and on-prem environments. This is a full-time role based in Toronto, Vancouver or Calgary.

 

Responsibilities

• Partner with clients to understand their business problems and spot where AI and advanced analytics can make a real difference
• Translate business and analytical requirements into end-to-end ML/AI solution designs
• Lead hands-on ML/AI engineering work, including exploratory data analysis, data preparation and model development for forecasting, classification, recommendation and anomaly detection
• Build with Python and common ML frameworks such as scikit-learn, TensorFlow, PyTorch, Azure ML Studio and Databricks MLflow
• Develop and optimize AI and GenAI solutions using platforms like AI Foundry, GCP Vertex AI, AWS SageMaker and Bedrock
• Operationalize AI/ML pipelines with MLOps best practices, including model deployment, versioning, CI/CD, automated testing and monitoring
• Own model monitoring, performance tuning, drift detection and retraining strategies in production
• Work with data engineers to keep data pipelines reliable and scalable for model training and inference
• Apply responsible AI principles, including explainability, bias detection, model governance, and security and privacy compliance
• Support client workshops, technical discussions and stakeholder presentations on AI strategy, solution design and implementation

 

Requirements
 

• University degree in computer science, engineering, data science, mathematics or a related field
• 5+ years of experience in machine learning, data science, AI engineering or a related field, with a track record of shipping production ML solutions
• Strong Python skills for data analysis, machine learning and model development
• Hands-on experience with ML frameworks and platform tools such as scikit-learn, TensorFlow, PyTorch, Azure ML Studio and Databricks MLflow
• Solid grasp of ML algorithms, statistics, model evaluation and feature engineering
• Experience designing and building end-to-end ML pipelines, from preprocessing and training through validation, deployment and monitoring
• Practical MLOps experience, including CI/CD, model versioning, experiment tracking and automated retraining
• Experience deploying models to Azure, AWS or GCP, with an understanding of cloud-native architecture and security
• Experience with generative AI, LLMs, prompt engineering or retrieval-augmented generation (RAG)
• Strong consulting and communication skills, with the ability to explain complex technical ideas to non-technical stakeholders
• Proven ability to collaborate across cross-functional, multidisciplinary teams

Nice to haves
 

• Familiarity with big data or distributed processing frameworks such as Spark is considered an asset
• Experience fine-tuning foundation models would be a plus
• Cloud AI/ML certifications would be a plus, such as Azure AI Engineer Associate, AWS Machine Learning Specialty, Google Professional ML Engineer, Databricks ML Engineer Associate or Databricks Generative AI Engineer

 

Benefits

• Compensation listed on the posting is for the base salary. Total compensation includes base salary + annual bonuses. 
• A comprehensive total rewards program
• Flexibility to work from Toronto, Vancouver or Calgary
• Put AI into production for some of Canada’s largest organizations, with your models running in real business environments rather than sitting in pilots
• Work across Azure, AWS and GCP on a wide range of client problems, building breadth that’s hard to get at a single company
• Shape how responsible AI is adopted at scale, influencing governance, explainability and fairness standards for major Canadian employers
• Get a seat in client strategy conversations, helping executives decide where and how to invest in AI