ML Ops Engineer

We’re working with a Silicon Valley–origin AI company focused on taking AI out of notebooks and into production
.

This role sits at the intersection of machine learning, infrastructure, and reliability
.

The team is responsible for making sure AI systems are deployable, observable, scalable, and cost-efficient across cloud and hybrid environments.

If you enjoy turning models into real, reliable systems
, this role is built for that.

• Deploy and operate ML models in production environments
• Build and maintain CI/CD pipelines for ML workloads
• Manage model serving, monitoring, logging, and alerting
• Work with backend and platform teams to product ionize AI systems
• Improve reliability, performance, and cost efficiency of AI deployments

• Experience as an MLOps Engineer
  , ML Engineer
  , or Platform Engineer
• Strong understanding of Kubernetes
  , containers, and cloud infrastructure
• Experience with model deployment
  , serving, and monitoring
• Familiarity with CI/CD
  , infrastructure-as-code, and automation
• Comfortable owning production systems end-to-end

• Experience with on-prem or hybrid AI deployments
• Familiarity with model optimization, inference performance, or GPU workloads
• Experience working with large-scale AI or data platforms