About the job
Are you passionate about bridging the gap between Machine Learning models and rock-solid infrastructure across both cloud and on-premise environments? We’re looking for an MLOps & DevOps Engineer with 2+ years of experience to help us build, scale, and maintain our hybrid AI systems at MediaSci !
Responsibility
Design, deploy, and manage scalable ML pipelines across cloud platforms and on-premise bare-metal servers.
Build, automate, and maintain CI/CD/CT (Continuous Training) pipelines for production ML models.
Containerize ML workloads and manage Kubernetes clusters (cloud-managed and on-premise deployments).
Implement dual-layer monitoring for infrastructure health (CPU, GPU utilization, memory, network latency) and ML metrics (data drift, model accuracy).
Partner with Data Scientists and Software Engineers to streamline model deployment from local sandbox environments to production.
Maintain network security, server provisioning, and system reliability across hybrid environments.
Requirements
2+ years of hands-on experience in MLOps, DevOps, or Systems/Infrastructure Engineering.
Proven experience managing on-premise server infrastructure, bare-metal setups, and hybrid cloud architectures.
Strong proficiency in Linux system administration, Python, and Shell scripting.
Deep experience with Docker and Kubernetes (including on-premise cluster setup/maintenance).
Hands-on experience with CI/CD tools (GitHub Actions, GitLab CI, or Jenkins) and IaC (Terraform or Ansible).
Familiarity with ML orchestrators and tracking tools (MLflow, Kubeflow, Airflow, or Weights & Biases).
Experience with at least one major cloud provider (AWS, GCP, or Azure).
