Blockchain Jobs in Asia

1. Participate in the design, development, and maintenance of AI-related components, including model serving, inference modules, and associated toolchain development.

2. Participate in LLM evaluation work, including effectiveness assessment, benchmark construction, and experimental analysis, as well as assist in model performance optimization.

3. Contribute to system performance, stability, and reliability improvements, driving engineering optimizations such as inference efficiency and resource utilization.

4. Participate in the design and implementation of AI solutions for business scenarios, including but not limited to Agent, RAG, and AIGC applications.

5. Collaborate with product, algorithm, and engineering teams to drive the adoption and iteration of AI capabilities in real-world business contexts.

1. Basic Requirements

• Currently pursuing a Bachelor's degree or above in Computer Science, Artificial Intelligence, Software Engineering, or a related field.

• Available to intern at least 3 days per week; internship duration of 6 months or more is preferred.

2. Technical Skills (Core Requirements)

• Solid programming fundamentals with proficiency in Python (required) and good coding practices.

• Basic understanding of backend development (API design, service development, etc.).

• Familiarity with at least one deep learning framework (PyTorch / TensorFlow).

• Basic understanding of large language models (LLMs), such as Transformer architecture, fine-tuning, and inference pipelines.

3. Bonus Skills (Strongly Preferred)

• Experience or knowledge in Prompt Engineering / RAG / Agent.

• Experience with model evaluation (benchmarks, automated assessment).

• Familiarity with frameworks such as LangChain / LlamaIndex / Dify / Coze is a plus.

• Prior project experience in AIGC, dialogue systems, or intelligent agents is a plus.

• Familiarity with Linux environments and basic debugging skills.

• Knowledge of Docker / Kubernetes / distributed systems is a plus.

• Some understanding of system performance optimization (latency, throughput, resource utilization).