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 (México)
🏢 Apex Systems
📍 México