Senior MLOps Engineer
Tasks
- Build deployment pipeline
- Configure alerting and audit visibility
- Define model shipping playbook
- Drive infrastructure and observability architecture
- Implement model versioning and rollback
- Improve cost and latency
- Instrument production models and agents
- Mentor data engineers on MLOps patterns
- Operate ML reliability runbooks
- Own ML serving API
- Stand up monitoring dashboards
Perks/Benefits
- Home office setup
- National holidays
- Paid time off
- Remote flexibility
- Stock options
- Unforgettable off-sites
- Work trip budget
Skills/Tech-stack
AWS Lambda | Alerting | Amazon ECS | Bamboo | Batching | BentoML | CI/CD | Caching | Databricks | Distributed tracing | Docker | FastAPI | GitHub Actions | GitLab CI | IAM | Infrastructure as Code | KMS | Low Latency | Low Latency APIs | Monitoring | Observability | PySpark | Python | SSM | Secrets Manager | Terraform | TypeScript | “as-code”
Education
N/A
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