ML Platform Engineer
Tasks
- Build caching and prompt deduplication
- Collaborate on model releases and rollout
- Create deployment workflows with canary releases
- Design model serving platforms
- Develop autoscaling and capacity management
- Document operational procedures and performance tuning
- Drive end-to-end observability
- Implement multi tenant quality of service
- Implement request routing and batching
- Implement security controls at serving layer
- Integrate with API gateways and identity systems
- Operate inference platforms in production
- Optimize inference performance
- Perform incident response for high availability services
- Tune GPU utilization and memory management
Perks/Benefits
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Capacity Planning | Content Filtering | Distributed Systems | GPU Architecture | Go | Identity Systems | Inference | KV cache | Kubernetes | LLM | Memory Management | Metrics | Multi-tenancy | Observability | Performance Engineering | Python | Quality of Service | Rate Limiting | Request Signing | Rust | Security controls | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
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