MLOps Engineer
Tasks
- Build tooling for dataset discoverability and access across geographies and formats
- Collaborate on backend data needs to persist data for downstream use
- Design build operate deep learning inference pipelines optimized for low latency high throughput GPU efficiency and compute cost
- Lead incident response for latency regressions accuracy degradation and unavailability
- Own observability for inference services with metrics tracing dashboards and alerting
- Plan and manage GPU fleet capacity for utilization and cost efficiency
- Safely roll out models using staged and canary releases with fast rollback
Perks/Benefits
Skills/Tech-stack
Algorithms | Apache Kafka | Capacity Planning | Cost Optimization | Data Structures | Deployment Automation | Distributed Systems | Docker | GPU Cost Optimization | Grafana | Helm | High Availability | Key-Value Stores | Key-value | Kubernetes | Metrics | MongoDB | Networking | NoSQL | Observability | Operating Systems | Prometheus | Python | Rollback | SQL | Software Engineering | Tracing | Vector Search
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
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