Senior MLOps Engineer | Ingénieur·e MLOps senior

Job summary

Jesta I.S. is seeking a Senior MLOps Engineer to design, build, and maintain data and machine learning pipelines for their AI and analytics platforms.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field.
• Minimum of 5 years of full-time professional experience in ML Engineering, MLOps, or data-pipeline development.
• 7+ years of relevant professional experience is preferred.
• Proven ability to design, build, and automate production-scale ML pipelines in cloud environments.
• Strong Python and SQL skills, including complex querying against large datasets.
• Hands-on experience integrating Oracle and Snowflake with production ML systems.
• Proficiency with Terraform or equivalent Infrastructure as Code tooling.
• Experience with containerized application deployment, CI/CD, and workflow orchestration.
• Experience with Kubernetes/EKS and cloud-native infrastructure.
• Experience with MLflow or equivalent MLOps tooling.
• Understanding of model lifecycle practices, including versioning, lineage, quality validation, staged promotion, monitoring, and rollback.
• Experience working with cloud platforms, preferably AWS and Azure.

Responsibilities

• 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.
• Capture reproducible run manifests, validate prediction completeness, and support safe partial-run recovery.
• Provision secure, multi-tenant cloud infrastructure using Terraform or OpenTofu.
• Implement CI/CD workflows using Azure DevOps and GitHub Actions.
• Build observability for run success, completeness, freshness, duration, drift, failures, and infrastructure cost.
• Maintain Dockerized, Kubernetes-native environments using ECR and EKS.
• Deploy secure React and Python ML applications across AWS and Azure.
• Collaborate with Data Scientists and Product stakeholders to operationalize models and improve performance.

Skills

Relocation