AI engineer

Job Responsibilities

1. Responsible for the research, development, iteration, and deployment of the company’s core AI algorithms, covering areas such as Large Language Model (LLM) applications, Computer Vision (CV), Natural Language Processing (NLP), and other related fields. Build efficient, stable, and reusable algorithm/model systems based on business scenarios.

2. Analyze business requirements and design algorithm solutions according to real-world cases. Lead end-to-end tasks including modeling , training, optimization and validation to solve core technical challenges in deployment and continuously improve model accuracy, latency, and practicality.

3. Stay at the forefront of industry trends regarding AI algorithms, LLM technologies, and open-source frameworks. Conduct technical research, proof-of-concept, and innovative implementations while contributing to technical documentation, solution frameworks, and internal toolkits to support team-wide technology advancement.

4. Collaborate closely with Product, Engineering, and DevOps teams to productize, deploy, and maintain algorithm models. Continuously monitor online model performance, quickly iterate on production issues, and ensure system stability

5. Participate in the full lifecycle of AI algorithm projects, including technical problem-solving, performance optimization, and supporting the implementation and iteration of the company’s AI products and intelligent business solutions.

Requirements

Key Qualifications

1. Bachelor’s degree or above in Computer Science, Artificial Intelligence, Software Engineering, Mathematics, Statistics, Electronic Engineering, or related fields, with 1–3 years of experience in AI algorithm development.

2. Strong foundation in Machine Learning and Deep Learning theories, familiar with classical algorithms such as classification, regression, clustering, feature engineering, and model optimization. Proficient in CNNs, Transformers, LLM fine-tuning, RAG (Retrieval-Augmented Generation), and related core technologies.

3. Hands-on experience independently completing end-to-end AI projects, including model design, training, optimization, and deployment. Able to troubleshoot common issues such as overfitting, slow inference, and insufficient model accuracy.

4. Familiar with the entire lifecycle of data processing, model training, evaluation, and deployment. Strong coding standards and technical documentation skills.

Preferred Attributes (Nice-to-Haves)

1. Experience with LLM fine-tuning, Prompt Engineering, RAG knowledge base construction, or multimodal AI applications is highly preferred.

2. Hands-on project experience in areas such as Computer Vision, NLP, recommendation systems, or predictive analytics is preferred.

3. Familiar with model quantization, compression, inference acceleration, and other engineering optimization techniques, with experience in production AI model deployment and maintenance preferred.

4. Candidates with achievements in AI competitions, top-tier conference publications, or open-source contributions are preferred.

Soft Skills

1. Strong logical thinking, self-driven learning ability, and problem-solving skills, with the agility to quickly adapt to emerging AI technologies.

2. Strong sense of responsibility, excellent communication and teamwork skills, and the ability to collaborate efficiently across teams.

3. Passionate about applying AI technologies to real-world business scenarios and motivated to solve practical business challenges.