ML Systems Engineer
Tasks
- Build autoscaling and capacity management
- Collaborate with ML and product teams
- Design model serving platforms
- Develop deployment workflows canary releases shadow testing automated rollback
- Document operational procedures performance characteristics tuning guidance
- Drive end-to-end observability
- Implement caching prompt deduplication response reuse
- Implement multi tenant routing rate limiting quality of service
- Implement security controls request signing content filtering abuse detection
- Integrate model serving with API gateways identity and observability
- Operate incident response for high availability AI services
- Optimize inference performance
- Support new model releases capability rollouts
- Tune GPU utilization memory management KV cache
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Capacity Planning | Content Filtering | Distributed Systems | FinOps | GPU Utilization | Go | Incident Response | KV cache | Kubernetes | LLM Inference | Language Models | Large Language Models | Memory Management | Metrics | Model Compression | Model Distillation | Observability | Performance Engineering | Python | Quality of Service | Quantization | Rate Limiting | Request Signing | Rust | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
Roles
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