ML Platform Engineer
Tasks
- Build autoscaling and capacity management
- Create deployment workflows with canary and rollback
- Design model serving platforms
- Document operational procedures and tuning guidance
- Drive end to end observability and error tracking
- Implement batching and caching
- Implement multi tenant routing and rate limiting
- Implement prompt deduplication and response reuse
- Implement security controls at the serving layer
- Integrate API gateways and observability
- Operate high performance low latency inference services
- Optimize inference performance
- Perform incident response for high availability services
- Support model releases with ML and product teams
- Tune GPU utilization and memory management
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | CUDA | Caching | Canary Releases | Content Filtering | Deployment Automation | Distributed Systems | GPU | Go | Incident Response | Kubernetes | LLM | Metrics | Multi-tenant | Observability | Performance Engineering | Python | Rate Limiting | Request Routing | Request Signing | Rust | Security | Shadow testing | Structured Logging | TensorRT-LLM | Tracing
Education
Regions
Countries
States
Cities
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