We're working with a Silicon Valleyorigin 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.
What you'll do
- 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 productionize AI systems
- Improve reliability, performance, and cost efficiency of AI deployments
What we're looking for
- 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
Nice to have
- 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
