MLOps Engineer
Tasks
- Build autoscaling and capacity management
- Collaborate with ML teams for model releases
- Design model serving platforms
- Develop deployment workflows with canary releases
- Document operational procedures and tuning guidance
- Drive end-to-end observability
- Implement caching and prompt deduplication
- Implement multi tenant routing and rate limiting
- Implement request signing and abuse detection
- Integrate with API gateways and observability platforms
- Operate incident response for high availability services
- Optimize inference performance
- Stay current with AI serving research
- Tune GPU utilization and memory management
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Abuse detection | Autoscaling | C++ | CUDA | Caching | Canary Releases | Capacity Planning | Compression | Content Filtering | Distillation | Distributed Systems | FinOps | GPU | GPU Utilization | Go | Incident Response | KV cache | Kubernetes | LLM | Memory Management | Metrics | Multi-tenant | Observability | Performance Engineering | Python | Quantization | Rate Limiting | Request Signing | Rollbacks | Rust | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
Roles
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