ML Platform Engineer
Tasks
- Build autoscaling and capacity management
- Collaborate with ML and product teams for model releases
- Design model serving platforms
- Develop deployment workflows canary releases shadow testing rollback
- Document operational procedures and performance characteristics
- Drive end-to-end observability
- Implement caching prompt deduplication response reuse
- Implement multi tenant routing rate limiting quality of service
- Implement security controls request signing content filtering abuse detection
- Integrate model serving with API gateways identity systems observability platforms
- Operate high availability AI services
- Optimize inference performance
- Perform incident response
- Support productionization of AI serving research
- Tune GPU utilization memory management KV cache
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Abuse detection | Autoscaling | C++ | Caching | Canary Deployment | Capacity Planning | Cloud Platforms | Content Filtering | Distributed Systems | GPU Architecture | Go | High Throughput | High-Throughput Systems | Identity Systems | KV cache | Kubernetes | LLM Inference | Low Latency | Low-Latency Systems | Memory Management | Metrics | Multi-tenant | Multi-tenant routing | Observability | Performance Engineering | Python | Quality of Service | Rate Limiting | Request Signing | Rust | Shadow testing | Structured Logging | Tenant Routing | TensorRT-LLM | Tracing | VLLM
Education
Roles
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