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← CÔNG TY TNHH GENIE VINA

AI Engineer (LLM / Prompt & Fine-tuning)

CÔNG TY TNHH GENIE VINA · Hà Nội
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Loại hình
Toàn thời gian
Hình thức
Tại văn phòng
Cấp bậc
Nhân viên
Ngành nghề
Kỹ thuật / Cơ khí
Mức lương
Thương lượng
Địa điểm
Thành phố Hà Nội, Hà Nội

Tổng quan

  • Design, build, and continuously iterate production prompt systems: system prompts, few-shot design, structured output enforcement.
  • Build agentic workflows: function calling / tool use, multi-step planning, error recovery, and AI harness engineering beyond simple API integration.
  • Establish prompt versioning, A/B testing, and regression test suites so prompt changes are measurable rather than guesswork.
  • Design and tune RAG pipelines: chunking strategy, embedding selection, retrieval tuning, and reranking.
  • Build training datasets (collection, cleaning, labeling, synthetic generation) and run fine-tuning experiments on open models, with team support on infrastructure.
  • Benchmark and select models across commercial APIs and open-weight models based on quality, latency, and cost.
  • Help build our evaluation practice from the ground up: golden datasets, automated scoring, LLM-as-judge
  • Define per-feature quality metrics, prevent regressions across model or prompt changes, and analyze failures such as hallucination, format breakage, and refusal.
  • Expose AI capabilities to product teams through clean, well-documented service interfaces.
  • Instrument observability for token cost, latency, and full request tracing.
  • Define request/response contracts for AI features with front-end and back-end engineers: streaming, failure states, and long-running jobs.
  • 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 Node.js, REST APIs, and endpoints consumed by web clients (streaming, cancellation, timeout handling).
  • 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.
  • 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.
  • 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.

Quyền lợi

Đào tạo

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