AWS Sagemaker Architect

Insight Global; Toronto; On-site; CAD 100,000 - 130,000; Full time

Job summary

A leading technology solutions company in Toronto is looking for a hands-on Solution Architect to design and implement enterprise-scale machine learning solutions using AWS SageMaker. The ideal candidate will have over 5 years of experience in solution architecture with a focus on cloud and ML systems. Responsibilities include defining MLOps strategies, collaborating with cross-functional teams, and ensuring best practices in model governance. A deep understanding of wealth management is essential for aligning ML solutions with business objectives.

Benefits

Diverse and inclusive work environment

Equal opportunity employer

Accommodations available for disabilities

Qualifications

• 5+ years of experience in solution architecture with a focus on AWS cloud and ML systems.
• Hands-on experience with AWS SageMaker for model training, deployment, and monitoring.
• Strong understanding of CI/CD pipelines for ML workflows.

Responsibilities

• Architect and implement end-to-end ML solutions leveraging AWS SageMaker.
• Define and own the strategy and roadmap for MLOps.
• Collaborate with data scientists and ML engineers to operationalize models.

Skills

AWS SageMaker expertise

MLOps knowledge

Communication skills

Stakeholder management

Communication skills

Stakeholder management

Education

AWS Certified Solutions Architect – Professional or Specialty in Machine Learning

Tools

AWS Lambda

AWS Step Functions

AWS CloudFormation

MLflow

Kubeflow

Job description

Job Description

Insight Global is seeking a hands‑on Solution Architect with deep expertise in AWS SageMaker and MLOps to lead the design, implementation, and strategic roadmap for enterprise‑scale machine learning solutions in AWS. This role requires a strong technical foundation combined with the ability to drive architecture decisions, influence stakeholders, and ensure operational excellence in ML model deployment and lifecycle management.

A background or strong understanding of wealth management processes, products, and data flows is essential to align ML solutions with business objectives in the financial services domain.

Responsibilities

• Architect and implement end‑to‑end ML solutions leveraging AWS SageMaker and related AWS services.
• Define and own the strategy and roadmap for MLOps, ensuring scalability, security, and compliance.
• Collaborate with data scientists, ML engineers, and DevOps teams to operationalize ML models into production environments.
• Design CI/CD pipelines for ML workflows, including model training, testing, deployment, and monitoring.
• Establish best practices for model governance, versioning, and reproducibility.
• Provide technical leadership and mentorship to engineering teams on cloud‑native ML architecture.
• Partner with wealth management business stakeholders to align ML solutions with organizational goals and KPIs.
• Stay current with emerging technologies and drive innovation in ML and cloud architecture.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [e-mail]. To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

• 5+ years of experience in solution architecture with a focus on AWS cloud and ML systems.
• Hands‑on experience with AWS SageMaker for model training, deployment, and monitoring.
• Proficiency in AWS services such as Lambda, Step Functions, CloudFormation, and IAM for secure and automated workflows.
• Familiarity with MLOps principles and tools (e.g., MLflow, Kubeflow) to support operationalization of ML models.
• Strong understanding of CI/CD pipelines for ML workflows using AWS tools.
• Knowledge of wealth management domain (investment products, portfolio optimization, regulatory compliance) to align with business objectives.
• Excellent communication and stakeholder management skills.
• AWS Certified Solutions Architect – Professional or Specialty in Machine Learning.
• Experience with large‑scale ML deployments in financial services or wealth management.