ML Ops Engineer
Core
Build and maintain infrastructure and tooling to ensure reliable production of machine learning systems, managing the full lifecycle from experimentation to deployment.
Role type
Senior MLOps Engineer
Builds
CI/CD pipelines, model deployment workflows, observability standards, and automation frameworks for ML systems
Domain
Education technology / Machine Learning Infrastructure
Deliverable
production ML models
Required skills
MLOps, DevOps, CI/CD pipelines, model deployment, monitoring, observability, MLflow, containers, orchestration, infrastructure-as-code, cloud platforms
Preferred skills
LLM serving, feature stores, data pipeline tooling, cost/latency optimization
Technologies
Docker, Kubernetes, GitLab CI, Terraform, Azure, AWS, GCP, MLflow, PyTorch, Kubeflow
Responsibilities
Design and maintain CI/CD pipelines for ML models; Build and operate model deployment, serving, and rollback workflows; Implement monitoring, observability, and alerting for models; Manage model lifecycle with MLflow; Automate infrastructure and partner with teams on standards
Seniority
Senior, hands-on IC
