ML Platform Engineer
Tasks
- Build autoscaling capacity management
- Create deployment workflows for canary and rollback
- Design model serving platforms
- Document operational procedures and performance tuning
- Enable end to end observability
- Implement caching and response reuse
- Implement multi tenant routing and rate limiting
- Implement request signing and abuse detection
- Integrate model serving with API gateways and identity systems
- Optimize inference performance
- Perform incident response and reliability improvements
- Support new model releases and rollouts
- Tune GPU utilization and memory management
Perks/Benefits
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Capacity Planning | Content Filtering | Distributed Systems | GPU Utilization | Go | KV cache | Kubernetes | LLM Inference | Memory Management | Metrics | Observability | Performance Engineering | Python | Quality of Service | Rate Limiting | Request Signing | Rust | Shadow testing | Structured Logging | TensorRT | Tracing
Education
Roles
Regions
Countries
States
Cities
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