ML Platform Engineer
Tasks
- Build autoscaling and capacity management systems
- Collaborate with ML and product teams on model releases
- Design and operate model serving platforms
- Develop deployment workflows canary releases and automated rollback
- Document operational procedures and performance tuning guidance
- Drive end to end observability and error tracking
- Implement caching prompt deduplication and response reuse
- Implement multi tenant routing rate limiting and quality of service
- Implement security controls request signing content filtering and abuse detection
- Integrate model serving with API gateways and observability platforms
- Operate incident response for high availability services
- Optimize inference performance
- Tune GPU utilization memory management and KV cache
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Abuse detection | Autoscaling | Batching | C++ | Caching | Canary Releases | Content Filtering | Distributed Systems | GPU Architecture | GPU Utilization | Go | Incident Response | Inference | KV cache | Kubernetes | LLM | Latency optimization | Memory Management | Metrics | Observability | Python | Quality of Service | Rate Limiting | Request Routing | Request Signing | Rust | Security controls | Shadow testing | Structured Logging | TensorRT-LLM | Throughput Optimization | Tracing | VLLM
Education
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