MLOps Engineer

About the role

enterprises a path from experiment to production in under 90 days — and the MLOps Engineer is the person who makes that promise real. You will design the CI/CD pipelines, model registries, deployment orchestration, and monitoring infrastructure that keep hundreds of ML models running reliably across SaaS,

What you'll do

• Maintain end-to-end ML pipelines using MLflow, Kubeflow, or Airflow that handle
• Training, validation, packaging, and deployment of models at scale
• Model registry architecture within RubiStudio: versioning strategies, stage
• Transitions (staging → canary → production), approval gates, and rollback
• Automated model monitoring for data drift, concept drift, and prediction

What you need

• 1–3 years in data science & ai
• MLOps
• Machine learning
• Helm
• Python
• Writing
• Airflow
• Any graduate

Good to have

• AWS
• Kubernetes
• Shell scripting
• Prometheus
• Grafana
• Docker