Assistant Officer/ Officer - AI Lab

Job Duties:

1. Participate in technical exploration, model research and development, and solution design for AI products and internal intelligent platforms.
2. Determine the most appropriate technical approach for specific problems, including prompt engineering, workflow orchestration, agents, RAG, model fine-tuning, model training, or other methods.
3. Design and build model experimentation, benchmarking, evaluation frameworks, and continuous iteration mechanisms; systematically assess model quality, stability, latency, cost, and safety.
4. Improve model capabilities for complex domain-specific problems, addressing the limitations of general-purpose models in accuracy, reliability, or domain knowledge.
5. Continuously track and research advances in LLMs, RAG, agents, reinforcement learning, multimodal models, inference optimization, and related areas.
6. Read, evaluate, reproduce, and adapt relevant academic papers, open-source projects, and industry technical solutions, then drive their productization and engineering implementation.
7. Work closely with backend, platform, data, and product teams to turn research prototypes into stable, maintainable, and observable production systems.
8. Establish best practices for experiment tracking, model versioning, evaluation dataset management, observability, regression testing, and quality assurance.

Job Requirements:

1. A master’s degree in computer science, artificial intelligence, machine learning, statistics, or a related field, or equivalent theoretical capability and practical experience.
2. Hands-on project experience in applied machine learning research or advanced AI engineering.
3. Ability to independently design rigorous experiments, analyze results, identify causes of model failures, and develop verifiable optimization and iteration plans.
4. Strong understanding of modern deep learning and LLM fundamentals, including Transformer architectures, training and fine-tuning, embeddings, retrieval, inference, and model evaluation.
5. Practical experience in at least several of the following areas: LLM applications, RAG systems, agent frameworks, supervised fine-tuning, preference optimization, reinforcement learning, multimodal models, synthetic data, and related technologies.
6. Proficiency in Python.
7. Ability to independently conduct technical research, formulate hypotheses, validate solutions, and turn them into deliverable engineering outcomes even when the problem is not fully defined.
8. Strong English reading and writing skills, with the ability to efficiently read technical documentation, academic papers, and open-source project materials.
9. 3 years of relevant working experience