ML Platform Engineer
Tasks
- Build autoscaling and capacity management
- Design model serving platforms
- Develop deployment workflows
- Document operational procedures and tuning guidance
- Drive end-to-end observability
- Implement caching and prompt deduplication
- Implement request routing and batching
- Implement security controls at serving layer
- Integrate with API gateways and identity systems
- Optimize inference performance
- Perform incident response and reliability improvements
- Tune GPU utilization and memory management
Perks/Benefits
Skills/Tech-stack
Abuse detection | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Content Filtering | Distributed Systems | GPU Architecture | GPU Utilization | GPU memory | GPU memory management | Go | Inference | KV cache | Kubernetes | LLM | Memory Management | Metrics | Observability | Performance Engineering | Python | Quality of Service | Rate Limiting | Request Signing | Rust | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
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