At KPMG in Canada, our people bring their unique perspectives to Canada’s most important challenges. Here, you can build momentum that reaches beyond our business, develop skills for the future, and take ownership of your career with support at every stage. Join a firm where your career can make a difference.

• Partner with clients to understand business problems and identify opportunities to apply AI and advanced analytics solutions.
• Translate business and analytical requirements into end‑to‑end ML/AI solution design,
• Execute ML/AI engineering tasks including exploratory data analysis, data preparation, model development (e.g., forecasting, classification, recommendation, anomaly detection) using tech stack such as Python and common ML frameworks (e.g., scikit‑learn, TensorFlow, PyTorch, Azure ML Studio, Databricks MLFlow).
• Develop and optimize AI and GenAI solutions using state-of-the-art tools and platform (AI Foundry, GCP Vertex AI, AWS Sagemaker and Bedrock).
• Operationalize AI/ML pipelines using AI/ML Ops best practices, including model deployment versioning, CI/CD, automated testing, and monitoring.
• Implement model monitoring, performance tuning, drift detection, and retraining strategies in production environments.
• Collaborate with data engineers to ensure reliable, scalable data pipelines that support model training and inference.
• Apply responsible AI principles, including explainability, bias detection, model governance, and compliance with security and privacy standards.
• Support client workshops, technical discussions, and stakeholder presentations related to AI strategy, solution design, and implementation.

• University degree in computer science, engineering, data science, mathematics, or a related discipline.
• 5+ years of professional experience in machine learning, data science, AI engineering, or a related field, with demonstrated experience delivering production ML solutions.
• Strong proficiency in Python for data analysis, machine learning, and model development.
• Hands‑on experience with machine learning frameworks/libraries and platform tools (e.g., scikit‑learn, TensorFlow, PyTorch, Azure ML Studio, Databricks MLFlow).
• Solid understanding of ML algorithms, statistics, model evaluation techniques, and feature engineering.
• Experience designing and implementing end‑to‑end ML pipelines, including data preprocessing, model training, validation, deployment, and monitoring.
• Practical experience with ML Ops practices, including CI/CD, model versioning, experiment tracking, and automated retraining.
• Experience deploying ML models to cloud environments (Azure, AWS, or GCP) with an understanding of cloud‑native architecture and security principles.
• Familiarity with big data or distributed processing frameworks (e.g., Spark) is an asset.
• Experience with generative AI, large language models (LLMs), prompt engineering, or retrieval‑augmented generation (RAG) is essential, experience with fine-tuning foundational models is an asset.
• Strong consulting and communication skills, with the ability to explain complex technical concepts to non‑technical stakeholders.
• Proven ability to collaborate within cross‑functional and multi‑disciplinary teams to solve complex business problems.

• Cloud AI / ML certifications (e.g., Azure AI Engineer Associate or better, AWS Machine Learning Specialty or better, Google Professional ML Engineer or better, Databricks ML Engineer Associate or better, Databricks Generative AI Engineer).