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