ML Platform Engineer
Tasks
- Build autoscaling and capacity management
- Collaborate with ML and product teams for model rollouts
- Design model serving platforms
- Develop deployment workflows canary shadow testing automated rollback
- Document operational procedures performance characteristics tuning guidance
- Drive end to end observability latency histograms error tracking
- Implement caching prompt deduplication response reuse
- Implement request routing batching caching
- Implement request signing content filtering abuse detection
- Integrate model serving with API gateways identity and observability
- Operate high throughput low latency inference services
- Operate incident response reliability improvements
- Optimize inference performance
- Translate AI serving research into production capabilities
- Tune GPU utilization memory management KV cache
Perks/Benefits
Skills/Tech-stack
API Gateway | Automated rollback | Autoscaling | Autoscaling Algorithms | C++ | Caching | Canary Releases | Capacity Planning | Distributed Systems | GPU | GPU Utilization | Go | KV cache | Kubernetes | Latency optimization | Memory Management | Metrics | Observability | Python | Quality of Service | Rate Limiting | Request Multiplexing | Rust | Shadow testing | Structured Logging | TensorRT-LLM | Throughput Optimization | Tracing
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Regions
Countries
States
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