AI role · Open offer
Senior LLM Engineer — WeChat AI
Tencent
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- Tencent Tower, 29F, Wan Chai, Hong Kong · Hybrid
- Compensation
- HKD 600k–950k/year + bonus
- Experience level
- Senior
- Team
- WeChat AI Lab
- Benefits & perks
About this role
What the team is looking for and how this position fits.
Tencent is hiring a Senior Senior LLM Engineer — WeChat AI on the WeChat AI Lab team (Hybrid · Hong Kong, Hong Kong). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
**Tencent** operates **WeChat**, games, and cloud services — one of Asia's largest **AI labs** with a flagship office in **Wan Chai, Hong Kong**.
Senior LLM Engineer (hybrid Hong Kong): **Cantonese/Mandarin NLP**, **RAG assistants**, and **multimodal** features for WeChat ecosystem products.
About Tencent
- Hong Kong tower hosts regional AI product and research teams.
- Models must respect content policies across Greater Bay Area users.
- Close partnership with Shenzhen engineering hubs.
What you will do
- Fine-tune LLMs for customer service and mini-program copilots.
- Build multilingual eval sets for dialect and code-switching.
- Deploy low-latency inference for mobile chat surfaces.
Stack & environment
- Python, PyTorch, LangChain, internal MLOps, Kubernetes.
- Hybrid Wan Chai; Cantonese or Mandarin required.
What we look for
- 5+ years NLP/LLM in consumer products.
- Right to work in Hong Kong.
Skills we're hiring for
What each capability means for this role—not a keyword checklist.
- Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
- PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
- LangChain
Tools & stack
Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).
- KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
- DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.
You're a strong fit if…
Tailored to this listing—use it as a self-check before you apply.
- ✓You can show evidence in your application: Cantonese/Mandarin NLP, RAG, or multimodal assistants with eval harnesses.
- ✓Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, LangChain, OpenAI API, and Kubernetes.
- ✓You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
- ✓You are comfortable with the hybrid rhythm (Hybrid · Hong Kong, Hong Kong)—onsite collaboration when it matters, deep work when remote.
- ✓You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
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What the employer asked for
Cantonese/Mandarin NLP, RAG, or multimodal assistants with eval harnesses.
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