About the role

Architect and build scalable, secure, multi-tenant cloud data pipelines in AWS and Azure
Implement robust ETL/ELT pipelines and APIs to move and access data across Oracle, AWS, and Snowflake
Leverage AWS services (Glue, Lambda, S3, RDS, EventBridge), AWS Batch, Azure components, Airflow, and Kedro
Automate infrastructure provisioning using Terraform/OpenTofu and manage CI/CD pipelines (Jenkins, GitHub Actions, ArgoCD)
Build infrastructure to support AI/ML workflows (training, validation, versioning) and integrate MLflow
Enable scalable model deployment in secure environments (containerized or cloud-native) and support full MLOps lifecycle
Deploy and manage React or Python-based ML applications with secure user access (private networking, MFA, RBAC, encryption)
Design end-to-end automation, integrate automated testing/scanning/rollback into CI/CD, and maintain monitoring/logging
Build portable components for cross-platform/multi-cloud deployment and support ERP/retail analytics use cases
Collaborate closely with data scientists, product managers, and architects to deliver robust solutions

Requirements

Master's degree in Data Science, Computer Science, or Software Engineering
5+ years of real-world experience in cloud data engineering, infrastructure, and deployment roles
Prior professional experience with AI/ML pipelines or applications is strongly preferred
Experience with AWS (S3, Lambda, Glue, RDS, IAM, EventBridge) and AWS Batch
Experience with Azure and multi‑cloud deployments
Experience with Snowflake and Oracle (ERP) and SQL
Proficiency in Python and PySpark
Experience with Docker, Kubernetes, and containerized deployments
Infrastructure-as-code with Terraform/OpenTofu; CI/CD with Jenkins, GitHub Actions, or ArgoCD
Orchestration tools: Airflow, Kedro; experiment tracking: MLflow
Monitoring and logging tools (Prometheus, CloudWatch) and DevSecOps practices
Ability to work hybrid in Montreal office (2 days/week)
Excellent written and verbal communication