ML Platform Engineer
Tasks
- Build autoscaling and capacity management
- Design model serving platforms
- Develop deployment workflows canary and rollback
- Document operational procedures and performance tuning
- Implement caching and response reuse
- Implement end-to-end observability
- Implement request routing and batching
- Implement security controls request signing and abuse detection
- Integrate API gateways identity and observability
- Operate high availability inference services
- Optimize inference performance
- Perform incident response for AI services
- Support new model releases and capability rollouts
- Tune GPU utilization and memory management
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | CUDA | Caching | Canary Releases | Capacity Planning | Content Filtering | Distributed Systems | FinOps | GPU | GPU Utilization | Go | Identity Systems | Incident Response | Inference | KV cache | Kubernetes | LLM | Latency optimization | Memory Management | Metrics | Observability | Performance Engineering | Python | Quality of Service | Queue Dynamics | Rate Limiting | Request Multiplexing | Request Signing | Rust | Security controls | Shadow testing | Structured Logging | TensorRT-LLM | Throughput Optimization | Tracing | VLLM
Education
Roles
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