ML Platform Engineer
Tasks
- Build autoscaling and capacity management
- Design model serving platforms
- Develop deployment workflows canary shadow rollback
- Document operational procedures and performance tuning
- Implement request routing and multiplexing
- Implement security controls at serving layer
- Integrate observability and error tracking
- Operate high performance inference systems
- Operate incident response for AI services
- Optimize inference performance
- Tune GPU utilization and memory management
Perks/Benefits
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Content Filtering | Continuous batching | GPU Utilization | Go | Incident Response | KV cache | Kubernetes | LLM | Memory Management | Metrics | Multi-tenant | Multi-tenant routing | Observability | Paged Attention | Python | Quality of Service | Rate Limiting | Request Multiplexing | Request Signing | Rust | Shadow testing | Speculative decoding | Structured Logging | Tenant Routing | TensorRT-LLM | Tracing | VLLM
Education
Roles
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