As the practice of artificial intelligence in important fields of finance and industry rapidly advances, the design of intelligent systems with significant levels of reliability, maintenance, and ability to seamlessly evolve has turned into the most important focus of research. This paper uses quantitative trading, which is a typical high-frequency and data-heavy decision-making context, as a case study. This study concentrates on the main engineering issues in model development, including the strict data timeliness demands, model repeated reuse, and the challenges of coordinating between multi-model-based engineering, and presents and introduces an engineering methodology of AI system engineering models that are based on its data-driven intelligence. The suggested solution changes and improves the conventional quantitative research processes by incorporating MLOps toolchains (ex: MLflow and DVC) into the open-source quantitative research platform Qlib. It is useful and resolves key bottlenecks that might include delays in data updates, inadequate reproducibility of experiments, absence of model version control, and inadequate monitoring features when using models in production settings. Empirical outcomes prove that the suggested integration approach is a great way of enhancing the extent of automation of the model development process and the efficiency of team collaboration, as well as agile reactions to altering market conditions and the ongoing and consistent development of the models.This study does not only offer tangible solutions and empirical data to the engineering discipline of AI models in quantitative trading, but also presents a transferable and scalable approach to methodological modeling platform on how data-driven intelligent systems can be developed and run across a large scope of application areas.

Zhixian Zhang. Research on Model Engineering Integration Methods for AI Systems Based on Data-Driven Intelligence. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 140-149. https://doi.org/10.38007/IJBDIT.2026.070117.

[1] Paramesha M, Rane N, Rane J. Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence[J]. Artificial Intelligence, Machine Learning, Internet of Things, and Blockchain for Enhanced Business Intelligence (June 6, 2024), 2024.

[2] Bachmann N, Tripathi S, Brunner M, et al. The contribution of data-driven technologies in achieving the sustainable development goals[J]. Sustainability, 2022, 14(5): 2497.

[3] Liu, H. (2025). Research on the Evaluation of User Safety Intervention Measures Based on Causal Inference. Engineering Advances, 5(4).

[4] Liu, H. (2025). Research on the Application of Sentiment Analysis in Customer Segmentation and Precision Marketing. Advances in Computer and Communication, 6(4).

[5] Yin, J. (2026). Research on a CLO Secondary Market Spread Volatility Prediction Model Based on RoBERTa Sentiment Factors. Advances in Computer and Communication, 7(1).

[6] Ye, J. (2025). Multimodal medical data intelligent classification method and system implementation based on improved SVM and similarity learning algorithm. International Journal of World Medicine, 2025, 6 (1), 19, 27.

[7] Ye, J. (2025). Application of Nerve Signal Tracking Technology in Rehabilitation of Spinal Cord Injury. Artificial Intelligence and Digital Technology, 2(1), 171-177.

[8] Wu, L. (2025, December). Design and Application of Automatic Data Set Generation Tool Based on KLEE in Embedded Memory Management Performance Test Framework. In 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 1111-1117). IEEE.

[9] Wu, W. (2025, June). Construction and optimization of intelligent gateway software management platform based on jenkins cluster management under cloud edge integration architecture in industrial internet of things. In International Conference on 6G Communications Networking and Signal Processing (pp. 633-645). Singapore: Springer Nature Singapore.

[10] Truong, T. H. (2025). Research on the Application of Digital Healthcare Platforms in Chronic Disease Management. Advances in Computer and Communication, 6(5).

[11] Ye, J. (2025). Challenges and Future Development of Neural Signal Decoding and Brain-Computer Interface Technology. Journal of Medicine and Life Sciences, 1(3), 54-60.

[12] Hong, Y. (2025). Architecture Design and Performance Optimization of a Large-scale Online Simulation Platform for Business Decision-making. Advances in Computer and Communication, 6(4).

[13] Shen, D. (2024, November). Application of Machine Learning Algorithms Based on Magnetic Resonance Imaging in the Diagnosis of Knee Joint PVNS. In International Conference on Cognitive based Information Processing and Applications (pp. 401-411). Singapore: Springer Nature Singapore.

[14] Zhou, Y. (2024, November). Construction of a Multi-factor Quantitative Stock Selection System for the New Energy Industry Based on Microservices Architecture and Machine Learning Components. In International Conference on Cognitive based Information Processing and Applications (pp. 163-174). Singapore: Springer Nature Singapore.

[15] Huang, J. (2025, September). Performance Evaluation Index System and Engineering Best Practice of Production-Level Time Series Machine Learning System. In 2025 International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT) (pp. 01-07). IEEE.