Machine Learning Infrastructure Engineer
Tasks
- Build autoscaling and capacity management
- Deliver end to end observability and error tracking
- Design model serving platforms
- Develop deployment workflows canary and rollback
- Implement caching and prompt deduplication
- Implement multi tenant routing and rate limiting
- Implement serving layer security controls
- Integrate model serving with API gateways identity and observability
- Optimize ML inference performance
- Perform incident response and reliability improvements
- Support new model releases and rollout planning
- Tune GPU utilization and memory management
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Content Filtering | Distributed Systems | GPU Architecture | GPU memory | GPU memory management | Go | Incident Response | KV cache | Kubernetes | LLM Inference | Machine Learning | Memory Management | Metrics | Observability | Python | Quality of Service | Rate Limiting | Request Signing | Rust | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
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