ML Platform Engineer
Tasks
- Build autoscaling and capacity management
- Collaborate with ML and product teams for model releases
- Deliver end-to-end observability
- Design model serving platforms
- Develop deployment workflows canary releases shadow testing and rollback
- Document operations and performance tuning
- Implement caching and response reuse
- Implement multi tenant routing rate limiting and quality of service
- Implement serving layer security controls
- Integrate model serving with API gateways identity and observability
- Lead incident response and reliability improvements
- Optimize inference performance
- Tune GPU utilization memory management and KV cache
Perks/Benefits
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Capacity Planning | Content Filtering | Distributed Systems | GPU Architecture | GPU Utilization | Go | Identity Management | Incident Response | KV cache | Kubernetes | LLM Inference | Memory Management | Metrics | Multi-tenant | Observability | Performance Engineering | Python | Rate Limiting | Request Signing | Rust | Security controls | Shadow testing | Structured Logging | TensorRT-LLM | Tracing
Education
Roles
Regions
Countries
States
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