ML Platform Engineer
Tasks
- Build autoscaling and capacity management
- Collaborate on model releases
- Design model serving platforms
- Develop deployment workflows
- Document operational procedures and tuning guidance
- Implement caching and prompt deduplication
- Implement request routing rate limiting and quality of service
- Implement security controls at serving layer
- Integrate model serving with API gateways and observability
- Maintain end to end observability
- Optimize inference performance
- Perform incident response and reliability improvements
- Tune GPU utilization and memory management
Perks/Benefits
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Content Filtering | Distributed Systems | FinOps | GPU Utilization | Go | Incident Response | Kubernetes | LLM Inference | Memory Management | Metrics | Model Distillation | Model Quantization | Observability | Python | Quality of Service | Rate Limiting | Request Signing | Rust | Security | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
Roles
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