MLOps Engineer
Tasks
- Build autoscaling and capacity management
- Collaborate on new model release support
- Design model serving platforms
- Develop deployment workflows with canary and rollback
- Document operational procedures and tuning guidance
- Drive observability for latency and errors
- Implement caching and response reuse
- Implement request routing and multiplexing
- Implement security controls at the serving layer
- Integrate with API gateways and identity systems
- Operate incident response for reliability
- Optimize inference performance
- Tune GPU utilization and KV cache
Perks/Benefits
Skills/Tech-stack
APIs | Abuse detection | Autoscaling | C++ | Caching | Canary Releases | Capacity Planning | Content Filtering | Distributed Systems | GPU memory | GPU memory management | Go | Incident Response | KV cache | Kubernetes | LLM Inference | Memory Management | Metrics | Observability | Performance Engineering | Python | Quality of Service | Rate Limiting | Request Signing | Rust | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
Roles
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