MLOps 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 automated rollback
- Document operational procedures and performance tuning
- Implement caching and response reuse
- Implement multi tenant routing rate limiting and quality of service
- Implement security controls at the serving layer
- Integrate model serving with API gateways and identity systems
- Operate ML inference platforms
- Optimize inference performance
- Perform incident response for high availability services
- Provide end-to-end observability
- Translate AI serving research into production
- Tune GPU utilization and memory management
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Abuse detection | Access Management | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Capacity Planning | Content Filtering | Distributed Systems | GPU | Go | Identity and Access Management | Identity and access | Incident Response | Kubernetes | Metrics | Model Compression | Model Distillation | Observability | Performance Engineering | Python | Quality of Service | Quantization | Rate Limiting | Request Signing | Rust | Security | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
Roles
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