ML Platform Engineer
Tasks
- Build autoscaling and capacity management
- Collaborate on model release support
- Design model serving platforms
- Develop deployment workflows canary releases and rollback
- Document operational procedures and performance tuning
- Implement batching and caching
- Implement multi tenant routing rate limiting and QoS
- Implement security controls at the serving layer
- Integrate observability and API gateway workflows
- Operate inference platforms
- Optimize inference performance
- Perform incident response for AI 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 | Cloud | Content Filtering | Continuous batching | Distributed Systems | GPU | GPU memory | GPU memory management | Go | Inference | KV cache | Kubernetes | LLM | Large Model Inference | Large model | Memory Management | Metrics | Model Inference | Observability | Performance Engineering | Python | Quality of Service | Rate Limiting | Request Multiplexing | Request Signing | Rust | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
Roles
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