University degree in computer science, engineering, data science, mathematics, or related discipline.
5+ years of professional experience in machine learning, data science, AI engineering, or related field with proven production ML solutions.
Proficient in Python for data analysis and model development.
Hands-on with ML frameworks/libraries and tools like scikit-learn, TensorFlow, PyTorch, Azure ML Studio, and Databricks MLFlow.
Solid understanding of ML algorithms, statistics, and model evaluation techniques.
Experience in designing end-to-end ML pipelines, from data preprocessing to monitoring.
Practical knowledge of ML Ops practices, including CI/CD and automated retraining.
Responsibilities
Partner with clients to identify business problems and AI solution opportunities.
Translate business and analytical needs into end-to-end ML/AI solution design.
Execute engineering tasks including exploratory data analysis and model development.
Develop and optimize AI and GenAI solutions using modern tools and platforms.
Operationalize AI/ML pipelines following best practices and CI/CD.
Implement model monitoring, performance tuning, and retraining strategies.
Collaborate with data engineers to create reliable data pipelines.
Benefits
Comprehensive and competitive Total Rewards program.
Supportive work environment aimed at helping you perform at your best.
Full Job Description
Are you a technically strong and business-oriented Machine Learning / AI Engineer with a passion for building and scaling intelligent solutions? Our team is looking for a hands-on engineer with deep experience in AI/ML engineering and AI/ML engineering operations who can partner with clients to design, build, and operationalize AI-powered solutions at scale.
This role will focus on translating advanced analytics, machine learning, and generative AI use cases into secure, scalable, and production-ready solutions across on-prem and cloud environments (ideally on Azure but also GCP and AWS).
What you will do
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.
What you bring to the role
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.
Certifications (Preferred)
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).
KPMG Ontario Region Pay Range Information
The expected base salary range for this position is $103,000 to $135,000 and may be eligible for bonus awards. The determination of an applicant's base salary within this range is based on the individual's location, skills & competencies, and unique qualifications. In addition, KPMG offers a comprehensive and competitive Total Rewards program.
KPMG BC Region Pay Range Information
The expected base salary range for this position is $101,000 to $130,000 and may be eligible for bonus awards. The determination of an applicant's base salary within this range is based on the individual's location, skills & competencies, and unique qualifications. In addition, KPMG offers a comprehensive and competitive Total Rewards program.
Providing you with the support you need to be at your best
Our Values, The KPMG Way
Integrity, we do what is right | Excellence, we never stop learning and improving | Courage, we think and act boldly | Together, we respect each other and draw strength from our differences | For Better, we do what matters
