Senior MLOps Engineer | Ingénieur·e MLOps senior

About the Role

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.

Key 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).

Technical Stack

Languages & Frameworks: Python (pandas, PySpark, Polars, boto3, joblib, lightgbm/xgboost), SQL, dbt
Data Engineering: AWS Glue, AWS DMS, Athena, Snowflake, Oracle
Pipeline & Orchestration: Kedro, Airflow, EventBridge, Batch, Fargate
MLOps: MLflow, Docker, ECR, CI/CD (GitHub Actions, Jenkins, or similar)
Forecasting & Modeling: Prophet, XGBoost, LightGBM, scikit-learn
Cloud & Deployment: AWS (EC2, S3, RDS, Glue, Batch, Fargate), Azure (integration/authentication)
Security: AWS IAM, Cognito, encryption, and network access control

Qualifications

• 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.

Traits We Value

• Hands‑on engineer with strong ownership.
• Analytical, performance‑focused problem solver.
• Pragmatic balance of scalability, cost, and maintainability.
• Thrives at the intersection of data, ML, and software engineering.
• Collaborative mindset—works closely with data scientists and developers.
• Passion for automation, reliability, and continuous improvement.

Additional Information

• Work Model: Hybrid; 2days per week in the Montreal office. Remote option possible for exceptional candidates.

We thank all applicants for their interest; only those shortlisted will be contacted.

Join us to help build the cloud foundations of our AI‑powered future

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À propos du poste

Jesta I.S. développe des technologies d’entreprise pour le commerce de détail, utilisées par des marques de vêtements et de chaussures ayant des opérations complexes et multi‑sites.
Notre environnement de données englobe des plateformes ERP et cloud, et notre culture d’ingénierie est pratique, pragmatique et dynamique.

Vous travaillerez dans un environnement de production intégrant Oracle, Snowflake et AWS, soutenu par des normes de sécurité rigoureuses, des pratiques modernes de CI/CD, et une collaboration étroite entre les équipes de science des données et d’ingénierie.

Nous recherchons un·e Ingénieur·e MLOps senior pour concevoir, construire et maintenir les pipelines de données et d’apprentissage automatique (machine learning) qui alimentent nos plateformes d’IA et d’analytique.

Il s’agit d’un rôle d’ingé