Senior MLOps Engineer | Ingénieur·e MLOps senior

Jesta I.S.•Westmount, QC

1d•Hybrid

About The Position

Jesta I.S. builds enterprise retail technology used by apparel and footwear brands with complex, multi-site operations. Our data environment spans ERP and cloud platforms, and our engineering culture is hands-on, pragmatic, and fast-moving. You’ll work in a production environment that integrates Oracle, Snowflake, and AWS, supported by strong security standards, modern CI/CD practices, and close collaboration between data science and engineering teams. We are looking for a Senior MLOps Engineer to design, build, and maintain the data and machine learning pipelines that power our AI and analytics platforms. This is a hands-on engineering role responsible for the full lifecycle of ML operations—from data ingestion and transformation to model training, deployment, and retraining. You will work across multiple layers of the cloud stack, bridging data engineering, ML automation, and deployment, with a focus on reliability, scalability, performance, and cost-efficient design.

Requirements

Bachelor’s or Master’s in Computer Science, Machine Learning, or related field.
5+ years of professional experience in ML engineering, MLOps, or data-pipeline development.
Proven ability to design and automate end-to-end ML pipelines in the cloud.
Strong Python and SQL skills.
Experience integrating ML systems with enterprise data sources (Oracle, Snowflake).
Familiar with containerized deployments, workflow orchestration, and CI/CD.
Understanding of model lifecycle management, versioning, and deployment best practices.

Responsibilities

Build and automate ML pipelines for data preparation, training, inference, and retraining.
Develop and maintain data pipelines between Oracle ERP, Snowflake, and cloud environments.
Create Kedro-based modular pipelines for reusable and maintainable workflows.
Use AWS Glue, DMS, Athena, and dbt for ETL and data transformation.
Manage AWS Batch and Fargate workloads for scalable model training and inference.
Integrate advanced data-science and forecasting libraries into production workflows.
Implement CI/CD pipelines for ML and data workflows (GitHub Actions, Jenkins, etc.).
Use MLflow for experiment tracking, model registry, and artifact management.
Build and maintain Dockerized environments via AWS EC2, ECR, and Batch.
Collaborate with data scientists to operationalize models and optimize performance.
Ensure secure, compliant cloud deployments (IAM, RBAC, encryption, network security).