Senior MLOps Engineer - SRE | DevOps
Tasks
- Build reproducible ML pipelines
- Define SRE SLOs and observability
- Design and operate ML inference infrastructure
- Implement infrastructure as code with Terraform
- Manage GitOps deployments with ArgoCD
- Operate Kubernetes on AWS EKS
- Optimize cloud costs for ML workloads
- Optimize production AI workloads
- Own ML deployment lifecycle
- Run incident response and postmortems
Perks/Benefits
- Collaborative engineering culture
- Engineering autonomy
- Fully remote work
- Global Time Zone Collaboration
- High-ownership environment
Skills/Tech-stack
AWS EKS | AWS IAM | AWS Networking | AWS compute | AWS storage | ArgoCD | Automation | CI/CD | CircleCI | Distributed Systems | GPU scheduling | GitHub Actions | GitLab CI | GitOps | Incident Response | Infrastructure as Code | Kubernetes | LLM Inference | MLOps | Machine Learning | Postmortems | Reliability Engineering | SLO | Site Reliability | Site Reliability Engineering | Terraform | “as-code”
Education
N/A
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