ML Platform Engineer
Tasks
- Build autoscaling and capacity management
- Deliver end to end observability and error tracking
- Design model serving platforms
- Develop deployment workflows with canary releases
- Document operational procedures and performance tuning
- Implement caching prompt deduplication and response reuse
- Implement routing rate limiting and quality of service
- Implement security controls at the serving layer
- Integrate serving with API gateways and observability
- Operate inference services in production
- Optimize inference performance with batching and caching
- Perform incident response and reliability improvements
- Support model releases and capability rollouts
- Tune GPU utilization memory management and KV cache
Perks/Benefits
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Capacity Planning | Content Filtering | Continuous batching | Distributed Systems | Error Tracking | FinOps | GPU Architecture | Go | Identity Systems | Incident Response | Inference Optimization | KV cache | Kubernetes | Language Models | Large Language Models | Memory Management | Metrics | Observability | Performance Engineering | Python | Quality of Service | Rate Limiting | Request Multiplexing | Request Signing | Rust | Security controls | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
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