ML Platform Engineer
Tasks
- Build autoscaling and capacity management systems
- Collaborate with ML and product teams for model releases
- Design model serving platforms for production workloads
- Develop deployment workflows with canary releases and rollback
- Implement caching and request deduplication
- Implement end-to-end observability
- Implement multi tenant routing and rate limiting
- Implement security controls at the serving layer
- Integrate model serving with API gateways and observability
- Operate incident response for high availability AI services
- Optimize inference performance
- Tune GPU utilization and memory management
Perks/Benefits
- N/A
Skills/Tech-stack
Abuse detection | Automated rollback | Autoscaling | Batching | C++ | Caching | Canary Releases | Capacity Planning | Content Filtering | Distributed Systems | GPU | GPU memory | Go | KV cache | Kubernetes | LLM | Machine Learning | Metrics | Model Serving | Observability | Performance Engineering | Python | Quality of Service | Rate Limiting | Request Routing | Request Signing | Rust | Security | Shadow testing | Structured Logging | TensorRT | TensorRT‑LLM | Tracing
Education
Roles
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