MTS 2, AI Platform (8+)
ebayinc
|
4 days ago
|
Bengaluru
- Own production inference: take models from handoff to production-grade serving, including release engineering, capacity planning, cost optimization, and incident response.
- Tune inference performance: reduce end-to-end latency and increase throughput across real production traffic patterns.
- Optimize runtimes and servers: scale inference across heterogeneous GPU fleets; optimize stacks such as vLLM, Triton, and related components (e.g., schedulers, KV cache, batching, memory).
- Benchmark and measure: build benchmarking suites, metrics, and tooling to quantify latency, throughput, GPU utilization, memory, and cost.
- Reliability and observability: improve monitoring, tracing, and alerting; participate in incident response and postmortems to harden systems.
- Apply and ship new optimizations: evaluate research and implement pragmatic inference optimizations (e.g., quantization, paging, kernel/runtime improvements).
- Partner with cross-functional teams: work with data science and product teams to translate business requirements into performance and availability SLOs.
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