LLM Engineer – Smart Manufacturing (Full-Time)

We are a global leader in design, development & manufacture of industrial laser systems and solutions for the smartphone, electronics & semiconductor manufacturing and automotive industry.

Please visit the company's web site to learn  about more: www.laservall.com

To cope with our continuous growth, we are now looking for a high caliber candidate to join our smart manufacturing innovation team:

Job Summary:

LLM Engineer will spearhead the development and integration of LLM within Laservall’s product line. In this role, you will firstly work on voice assistants to guild field customer user in routine operation and trouble shooting. And explore LLM’s reasoning capability to improve product usability and come up with new products or solutions. You will work cross-functionally with design, process engineering, and product development teams to embed LLM into our manufacturing processes and seek new opportunities that LLM can power.

Key Responsibilities:

• Design and integrate multi-model(audio, video) LLMs chatbot into product;
• Explore business use cases on using LLM by working with different stake holders.
• Perform research on latest AI technologies. Conduct proof-of-concept on AI solutions integrated with current product such as SW refactoring, reasoning and automation powered by LLM.
• Partner with other talented Data Scientist, Data engineer to find opportunities of improvement from LLM.
• Collaborate with design, testing, and development teams to convert simulation results into actual engineering improvement plans;

Qualifications:

• Bachelor’s or Master’s degree in Information System / Computer Science / Data Science / Artificial Intelligence or related disciplines.
• At least 1 years’  real life experience in LLM adopting. Experience on audio, video Multi-model LLM is a plus.
• Advanced skills in Python and frameworks (PyTorch, Jax), Familiarity with C++ is a plus.
• Hands-on experience with various LLM such as DeepSeek R1, Qwen 2.5, Llama 3.1.
• Hands-on experience with LLM finetuning, RAG and multi-agent aproach.
• Experience in ​local LLM inference setups.
• Familiar with linux shell, version control tools (e.g., Git).
• Familiarity with MLOps pipelines, and CI/CD practices is a plus.
• Experience in RPA (e.g., Power Automate, UiPath)  is a plus.
• Experience in Deep Seek RORL is a plus.
• Experience in Multi-model LLM (e.g., Internal, QwenVL) is a plus.
• Familiarity with C++/CUDA  is a plus.
• Good command of spoken and written English.
• Good command of spoken Chinese is a plus.
• Excellent communication skills and ability to work cross-functionally. Open to learning new technologies and willing to work in a diverse environment
• May require occasional same-day round trips to our Shenzhen office.

What We Offer

• A competitive remuneration package including 5-day week, medical, discretion bonus
• Opportunity to shape the future of smart manufacturing
• Work with diverse teams in a fast-moving, innovative environment

For application, please apply with full resume, current and expected salary and availability for interview.

Proficiency in written/listening English and simple oral communication is the entrycriterion, please use your English resume when applying for the position.

The initial telephone interview and subsequent interview arrangements will be made byour Korean HR colleagues who will call you in English. Soon after your application, ifresume passes the screening, you will receive a call with the country prefix +82/0082which is most likely a call from our HR. Please pay attention to calls with such prefix and hope you answer in a timely manner.

Personal data collected will be treated as strictly confidential and only be used for recruitment purposes. Applicants who do not receive notification for interview within six weeks may consider their application unsuccessful.

We are also welcome candidate who are eligibility criteria for application of TTPS (TopTalent Pass Scheme), details please go through -https://www.immd.gov.hk/hks/services/visas/TTPS.html