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 integrating Oracle, Snowflake, AWS, and Azure, supported by strong security standards, modern CI/CD practices, and close collaboration across Data Science, Engineering, Frontend, and Product 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 deeply hands-on engineering role responsible for the full ML lifecycle, from data ingestion and transformation through model training, deployment, monitoring, retraining, and rollback. You will bridge data engineering, ML automation, infrastructure, observability, and application deployment to help build scalable, secure, multi-tenant AI infrastructure with a strong focus on reliability, performance, and cost-efficient design.

Build and automate ML pipelines for data preparation, training, inference, monitoring, and retraining. Develop production data flows across Oracle ERP, Snowflake, AWS, and Azure environments. Create reusable Kedro pipelines and manage scalable workloads through AWS Batch, EKS, Karpenter, Kueue, and Fargate.

Implement MLflow-based experiment tracking, model versioning, lineage, quality gates, staged promotion, and rollback. Implement CI/CD workflows using Azure DevOps and GitHub Actions, including testing, scanning, immutable images, and rollback strategies. Deploy secure React and Python ML applications across AWS and Azure using private networking, MFA, RBAC, encryption, and least-privilege access.

Collaborate with Data Scientists and Product stakeholders to operationalize models, improve performance, and address reliability gaps.

Technical Environment Languages & Frameworks:



Python (pandas, Polars, boto3, joblib, LightGBM/XGBoost), SQL, JavaScript/React Data Engineering: AWS DMS, Athena, Snowflake, Oracle Pipeline & Orchestration: Kedro, EventBridge, AWS Batch, Amazon EKS, Karpenter, Kueue, Fargate MLOps: MLflow, Docker, ECR, Azure DevOps, GitHub Actions Terraform/OpenTofu, CloudWatch, Prometheus, Grafana, structured logging, drift monitoring Cloud & Deployment: AWS (EC2, S3, RDS, Batch, EKS, Fargate, ECR, EventBridge, Lambda), Azure integration and parallel deployment Security: AWS IAM, Cognito, RBAC, MFA, Secrets Manager, PrivateLink, encryption, and network access controls Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field. ~ Minimum of 5 years of full-time professional experience, excluding internships and academic training, in ML Engineering, MLOps, or data-pipeline development. ~Proven ability to design, build, and automate production-scale, end-to-end ML pipelines in cloud environments.

Technical Expertise Strong

Python and SQL skills, including complex querying against large datasets. Hands-on experience integrating Oracle and Snowflake with production ML systems.

Experience with containerized application deployment, CI/CD, and workflow orchestration. Understanding of model lifecycle practices, including versioning, lineage, quality validation, staged promotion, monitoring, and rollback.

Experience working with cloud platforms, preferably AWS and Azure. Strong ownership and hands-on engineering mindset, from architecture through production.



Analytical and performance-focused approach to solving complex technical and operational problems.

Comfortable working across data engineering, machine learning, software engineering, infrastructure, and application delivery. Strong collaboration skills when working with Data Scientists, Frontend Developers, Product stakeholders, and Engineering teams. Strong attention to detail and commitment to automation, observability, reliability, and responsible data handling.

Strong adaptability and ability to work effectively in a rapid-moving engineering environment. 2days per week in the Montreal office. Remote option possible for exceptional candidates. Join us to help build the secure, scalable cloud foundations of our AI-powered future! développe des technologies d’entreprise pour le commerce de détail, utilisées par des marques de vêtements et de chaussures ayant des opérations complexes et réparties sur plusieurs sites.

Notre environnement de données englobe des plateformes ERP et infonuagiques, et notre culture d’ingénierie est pratique, pragmatique et dynamique. Vous travaillerez dans un environnement de production intégrant Oracle, Snowflake, AWS et Azure, soutenu par des normes de sécurité rigoureuses, des pratiques modernes de CI/CD et une collaboration étroite entre les équipes de science des données, d’ingénierie, de développement frontend et de produit. Nous sommes à la recherche d’un·e ingénieur·e MLOps senior pour concevoir, développer et maintenir les pipelines de données et d’apprentissage automatique qui alimentent nos plateformes d’IA et d’analytique.

Il s’agit d’un rôle d’ingénierie très pratique couvrant l’ensemble du cycle de vie du ML, de l’ingestion et de la transformation des données à l’entraînement, au déploiement, à la surveillance, au réentraînement et au #

📌 Senior MLOps Engineer | Ingénieur·e MLOps senior (Montreal)
🏢 JESTA I.S.
📍 Montreal