MLOps Engineer
Tasks
- Build and operate ML lifecycle platform
- Codify infrastructure with infrastructure as code
- Configure Kubernetes deployments and containerized workloads
- Enable model monitoring for latency drift data quality and cost
- Implement CI CD for ML workloads
- Implement access controls deployment policies and auditability
- Manage model training and inference pipelines
- Own experiment tracking and model registry
- Set up automated testing and deployment validation
Perks/Benefits
- 401k
- Fitness fund
- Gym/Fitness reimbursement
- Health insurance
- Learning and development fund
- Paid parental leave
- Remote-first work environment
- Unlimited PTO
Skills/Tech-stack
Alerting | Automated testing | CI/CD | Containerization | Data Quality | Docker | Experiment tracking | Go | IAM | Infrastructure as Code | Kubernetes | MLflow | Model Drift | Model Registry | Monitoring | PyTorch | Python | Secrets management | Terraform | “as-code”
Education
N/A
Roles
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