ML Platform Engineer
Tasks
- Build autoscaling capacity management systems
- Collaborate with ML and product teams for model rollouts
- Design model serving platforms
- Develop deployment workflows canary releases shadow testing automated rollback
- Document operational procedures performance characteristics tuning guidance
- Drive end-to-end observability
- Implement caching prompt deduplication response reuse
- Implement multi tenant routing rate limiting quality of service
- Implement security controls at serving layer request signing content filtering abuse detection
- Integrate model serving with API gateways observability and identity
- Operate incident response
- Optimize inference performance
- Translate AI serving research into production capabilities
- Tune GPU utilization memory management KV cache
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Abuse detection | Autoscaling | C++ | Caching | Canary Releases | Capacity Planning | Cloud Platforms | Content Filtering | Continuous batching | Distributed Systems | Distributed tracing | GPU Architecture | GPU Utilization | Go | Incident Response | Inference Optimization | KV cache | Kubernetes | LLM Inference | Latency Histograms | Machine Learning | Memory Management | Metrics | Model Serving | Multi-tenant | Multi-tenant routing | Observability | Paging Attention | Prompt Deduplication | Python | Quality of Service | Rate Limiting | Request Multiplexing | Request Signing | Rust | Shadow testing | Speculative decoding | Structured Logging | Tenant Routing | TensorRT-LLM
Education
Roles
Regions
Countries
States
Cities
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