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Selected role

MLOps Solution Architect

Iris Software Inc. Toronto, ON

Live

$98k - $120k · Senior Full-time Posted about 2 months ago 2 min read

Greetings

We're seeking a hands-on MLOps Solution Architect to design and implement scalable, secure, and cost-effective ML platforms on AWS . You'll lead the end-to-end architecture for model training, CI/CD pipelines, deployment strategies, monitoring, and governance across teams of data scientists and engineers.

Location: Toronto, ON (Hybrid- 3 days onsite per week)

Client: One of the largest banks in Canada

Duration: Long-term contract

Key Responsibilities • Architect MLOps frameworks using AWS SageMaker , EKS , ECR , CodePipeline , and Step Functions • Design pipelines for data prep, training, evaluation, registry, and automated deployment • Integrate MLflow or SageMaker Model Registry for model tracking and lifecycle management • Implement model serving strategies — batch, online, A/B, shadow, and canary rollouts • Set up monitoring with CloudWatch , Evidently AI , Prometheus , or WhyLabs • Establish governance: lineage, audit trails, model approvals, and access controls (IAM, KMS) • Drive standardization across MLOps templates and Infrastructure as Code (Terraform or CloudFormation) • Collaborate with Data Engineering and DevOps to align ML pipelines with enterprise architecture

Must-Have Skills • 14+ years of experience in ML/AI platform design and data infrastructure • Deep expertise in AWS services: • Compute: EC2, EKS, Batch, Lambda • Storage: S3, Lake Formation, Glue Catalog • Pipeline: Step Functions, CodePipeline, Airflow • Training/Serving: SageMaker (Studio, Training, Model Registry, Endpoints) • Monitoring: CloudWatch, CloudTrail, Prometheus • Security: IAM, Secrets Manager, KMS, VPC • Proficient in Python and infrastructure scripting (Terraform, CloudFormation) • Experience building and deploying models in production environments (CI/CD) • Familiar with data versioning (DVC, Delta Lake) and experiment tracking (MLflow) • Strong understanding of containerization (Docker, EKS) and Kubernetes-based serving • Excellent communication and stakeholder management

Nice to Have • Knowledge of Generative AI and LLM deployment using AWS Bedrock or custom endpoints • Familiarity with event-driven pipelines using SNS/SQS or Kinesis • Model performance optimization with GPU instances and autoscaling • Cost governance and monitoring for ML workloads • Experience in financial or regulated industries (governance, model risk)

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