Machine Learning Engineer – Amazon Web Services – Toronto, ON
Location: Toronto, ON | Company: Amazon
Amazon Web Services (AWS) is on the lookout for a talented Machine Learning Engineer to join the ProServe Shared Delivery Team – Data & AI in Canada. This is a rare opportunity to work at the forefront of Generative AI (GenAI) and cloud-based ML solutions, partnering with some of the world’s largest enterprises to transform how they adopt and leverage artificial intelligence. If you’re passionate about turning complex data challenges into scalable AI-driven outcomes, this role was built for you.
As a Delivery Consultant within AWS Professional Services, you’ll be doing far more than writing code — you’ll be shaping enterprise cloud strategies, advising senior stakeholders, and leading end-to-end AI/ML project lifecycles. From data preparation and model development to deployment and monitoring, your work will have a direct and measurable impact on how global businesses operate in the cloud.
About the Role: Machine Learning Engineer – ProServe Shared Delivery Team
This position sits within the AWS Professional Services (ProServe) organization, a global team of cloud and AI experts dedicated to helping customers achieve real business outcomes using the AWS Cloud. You’ll be trusted to architect complex, secure, and scalable AI/ML and GenAI solutions tailored to each client’s unique environment — working across industries and use cases that span the breadth of enterprise computing.
Collaboration is central to this role. You’ll partner with cross-functional teams including Applied Science, DevOps, Data Engineering, and Cloud Infrastructure to operationalize data and models, while serving as a trusted technical advisor to customers navigating their cloud and AI adoption journey. Travel to customer sites may be required on occasion.
Benefits and Salary
Amazon is known for offering a comprehensive and competitive total compensation package. Employees at AWS benefit from a flexible and inclusive work culture, robust mentorship and career development programmes, and a genuine commitment to work-life balance. AWS fosters a diverse, curiosity-driven environment with employee-led affinity groups, ongoing learning events, and access to knowledge-sharing resources designed to help you grow into a well-rounded professional. Additional perks include access to AWS Training and Certification resources and participation in global innovation initiatives like the Generative AI Innovation Center.
Job Details
📌 Job Type: Full-Time
🏢 Company: Amazon Web Services Canada, Inc.
📍 Location: Canada
🆔 Requisition ID: 3195138
Responsibilities
In this customer-facing role, you’ll take ownership of full-cycle AI/ML project delivery — from initial business discovery through to live deployment and ongoing optimization. These responsibilities reflect the strategic and technical depth required to succeed as a ProServe Delivery Consultant at AWS.
- Implement end-to-end AI/ML and GenAI projects, covering business needs analysis, data preparation, model development, deployment, and monitoring
- Design and build ML pipelines that support high-performance, reliable, scalable, and secure machine learning workloads
- Architect scalable ML solutions and MLOps frameworks using AWS services, leveraging GenAI capabilities where applicable
- Collaborate with cross-functional teams including Applied Science, DevOps, Data Engineering, Cloud Infrastructure, and Application teams to operationalize data and AI/ML models
- Serve as a trusted advisor to enterprise clients on AI/ML, GenAI, and cloud architecture best practices and industry trends
- Share knowledge and best practices across the organization through mentoring, training, publications, and the creation of reusable technical artefacts
- Ensure solutions meet industry standards and support clients in advancing their AI/ML, GenAI, and cloud adoption strategies
Requirements / Skills
The ideal candidate brings deep hands-on experience in cloud architecture, machine learning engineering, and enterprise software development. AWS values diverse backgrounds and non-traditional career paths — if you have the technical foundation and the drive to innovate, you’re encouraged to apply.
- 5+ years of experience in cloud architecture and implementation
- 5+ years of experience in data engineering, software engineering, or machine learning, with a strong grasp of distributed computing (e.g., data pipelines, distributed training and inference, ML infrastructure design)
- 3+ years of experience developing platforms for predictive modelling, natural language processing, and deep learning, with a proven track record of building, hosting, and deploying ML models on cloud services such as Amazon SageMaker
- Proficiency in SQL, Python, and at least one additional programming language (e.g., Java, Scala, JavaScript, TypeScript), along with expertise in key ML frameworks like TensorFlow and PyTorch
- Bilingualism in French and English is required for candidates based in Québec, given the global and cross-provincial nature of the role
How to Apply
Ready to help shape the future of AI in the cloud? Submit your application directly through the official Amazon Jobs portal. This is a high-impact role where your expertise will directly influence how the world’s leading enterprises harness the power of Machine Learning and Generative AI on AWS. Don’t wait — take the next step in your career today.
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Job Summary & Tips for Applying
Quick Summary & What to Highlight: This Machine Learning Engineer role at Amazon Web Services in Canada is perfect for candidates who excel in ML model development and deployment, cloud architecture on AWS, and cross-functional technical leadership. On your resume, emphasize any experience with Amazon SageMaker, MLOps pipelines, and distributed computing, attention to detail, and your ability to work in a fast-paced, enterprise-scale environment. If you’ve previously worked in AI consulting, cloud engineering, or data science, make sure to highlight specific achievements and responsibilities that align with this position.
Resume & Application Tips: Before applying, tailor your resume to match the job description. Include keywords like machine learning engineering, Generative AI, and AWS SageMaker that appear in the posting. Quantify your achievements where possible (e.g., “reduced model training time by 40% through distributed pipeline optimization” or “deployed ML models serving 1M+ daily predictions”). Write a brief cover letter expressing your genuine interest in Amazon Web Services and why you’re excited about this opportunity in Canada. Double-check your application for spelling errors and ensure your contact information is current.
Interview Preparation: If selected for an interview, research Amazon Web Services‘s leadership principles, recent AI innovations, and the AWS ProServe team’s mandate beforehand. Prepare specific examples using the STAR method (Situation, Task, Action, Result) to demonstrate your ML project delivery and cloud architecture skills. Common questions may include scenarios about handling ambiguous client requirements, designing scalable ML systems under constraints, and managing cross-functional stakeholders. Dress appropriately for a technology consulting environment, arrive 10–15 minutes early (or log on early for virtual interviews), and bring copies of your resume. Prepare thoughtful questions about the ProServe team’s projects, growth opportunities, and how success is measured in this role. After the interview, send a thank-you email within 24 hours reiterating your interest in the position.
