Qualifications5+ years of experience in DevOps, Cloud Engineering, or ML Engineering3+ years of hands‐on experience in MLOps or operationalizing ML models in production environmentsKey ResponsibilitiesArchitect and implement scalable end-to-end ML pipelines (training, validation, deployment, monitoring)Design and maintain CI/CD pipelines for ML workflows using Azure DevOpsImplement automated model versioning, artifact management, and rollback strategiesProvision and manage infrastructure using Infrastructure as Code (Terraform, ARM)Deploy containerized ML services using Docker and KubernetesImplement monitoring frameworks for model performance, drift detection, and data qualityOptimize inference performance, scalability, and cost efficiencyEnsure compliance, governance,



and security best practices in cloud ML environmentsProvide technical leadership and mentorship to junior engineersCollaborate closely with Data Science and Engineering teams to define production standardsRequired SkillsStrong experience with Microsoft Azure (required)Experience with AWS or GCP (plus)Advanced knowledge of DockerStrong hands‐on experience with Kubernetes (production clusters)Advanced proficiency in PythonExperience with Bash and/or PowerShellExperience designing and consuming REST APIsExperience with TensorFlow, PyTorch, or Scikit-learnFamiliarity with ML lifecycle tools such as MLflow, Kubeflow, DVC, or TFXExperience with orchestration tools such as Apache Airflow or PrefectImplementation of model drift detection and performance monitoring frameworksPreferred Certifications
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📌 Mlops Azure Devops Engineer (Estado de México)
🏢 Apex Systems
📍 Estado de México