Yang akan Anda kerjakan
- Lead technical discovery for AI / GPU prospects: workload type, dataset size, latency targets, parallelism strategy.
- Recommend the right hardware mix — H100 / H200 / L40S / RTX 6000 Ada / A100 — with capacity, power, and budget tradeoffs.
- Recommend the right software stack across open-source (PyTorch DDP / FSDP, DeepSpeed, vLLM, Triton) and licensed (NVIDIA AI Enterprise).
- Write SoWs and architecture diagrams in lockstep with B2B Sales and the AI / GPU Infrastructure Engineer.
- Run pre-sales POCs: spin up a sample training run, benchmark inference throughput, share signed results.
- Stay current on the LLM / AI-infra landscape; brief the team monthly on what changed and what it means for our pricing.
Yang kami butuhkan dari Anda
- 3+ years working with ML / GPU infrastructure as an engineer or solutions architect.
- Comfort across modern AI stacks: PyTorch, JAX awareness, Hugging Face ecosystem, vLLM, Triton.
- Solid grounding in distributed training (data / tensor / pipeline parallel) and inference patterns (batching, KV cache, quantization).
- Strong written and spoken communication — explains "H100 SXM vs L40S PCIe" to a CIO without losing them.
- Bahasa Indonesia + English; English-first acceptable if you anchor regional / SG accounts.
Nilai tambah
- LLM fine-tuning hands-on (LoRA / QLoRA, full FT, RLHF / DPO).
- Cost-modeling for AI workloads (training $/token, inference $/1k tokens).
- Indonesian regulated AI use cases (financial services, government, healthcare).
Tolok ukur sukses dalam 90 hari
- Three AI / GPU customer discovery cycles run.
- One signed POC, one more in late stage.
- Reference architecture diagrams published for the top three AI use cases (training, fine-tuning, inference / RAG).
Cara melamar
Kirimkan CV beserta catatan singkat (Bahasa Inggris atau Bahasa Indonesia) yang menjelaskan dua tanggung jawab pertama yang akan Anda kerjakan dan alasannya. Kami membaca setiap lamaran dan membalas dalam 7 hari.
Lamar → [email protected]