Qualifications 5+ years of experience in DevOps, Cloud Engineering, or ML Engineering
3+ years of hands‑on experience in MLOps or operationalizing ML models in production environments
Key Responsibilities Architect and implement scalable end-to-end ML pipelines (training, validation, deployment, monitoring)
Design and maintain CI/CD pipelines for ML workflows using Azure DevOps
Implement automated model versioning, artifact management, and rollback strategies
Provision and manage infrastructure using Infrastructure as Code (Terraform, ARM)
Deploy containerized ML services using Docker and Kubernetes
Implement monitoring frameworks for model performance, drift detection, and data quality
Optimize inference performance, scalability, and cost efficiency
Ensure compliance, governance, and security best practices in cloud ML environments
Provide technical leadership and mentorship to junior engineers




Collaborate closely with Data Science and Engineering teams to define production standards
Required Skills Strong experience with Microsoft Azure (required)
Experience with AWS or GCP (plus)
Advanced knowledge of Docker
Strong hands‑on experience with Kubernetes (production clusters)
Advanced proficiency in Python
Experience with Bash and/or PowerShell
Experience designing and consuming REST APIs
Experience with TensorFlow, PyTorch, or Scikit-learn
Familiarity with ML lifecycle tools such as MLflow, Kubeflow, DVC, or TFX
Experience with orchestration tools such as Apache Airflow or Prefect
Implementation of model drift detection and performance monitoring frameworks
Preferred Certifications
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📌 MLOps Azure DevOps Engineer (Heroica Puebla de Zaragoza)
🏢 Apex Systems
📍 Heroica Puebla de Zaragoza