Required
2+ years in
software engineering, including at least 1 year building and shipping LLM-based features to production.
Hands-on production experience with at least one major LLM API (Anthropic, OpenAI, or Google) and at least one open-weight model family (Llama, Qwen, Mistral, or similar).
Hands-on fine-tuning experience (SFT, LoRA or QLoRA)
Strong Python, with practical use of the Hugging Face stack or PyTorch.
Experience designing prompts as engineered artifacts.
Sufficient back-end ability to ship your own work: Python or [protected info], REST APIs, and endpoints consumed by web clients (streaming, cancellation, timeout handling).
Preferred
Experience with inference optimization (vLLM, TensorRT-LLM, quantization) or GPU training infrastructure.
Working knowledge of vector databases and embedding-based retrieval.
Experience with multi-agent frameworks and orchestration.
Experience with Vietnamese- or Korean-language model work, or multilingual evaluation.
Soft Skills
Comfortable working with ambiguity - this field changes monthly.
Evidence-driven: you reach for an eval set before you reach for an opinion.
Positive, proactive, and highly collaborative.
Comfortable reading recent articles and models, and translating them into something shippable.
Strong analytical and problem-solving skills.
Application
CV and a link to your GitHub or Hugging Face profile.
A short description of one LLM feature or model you built: what the problem was, what you tried, how you measured whether it worked, and what you would do differently now. Half a page is enough.