Total 2 job vacancies by Cloudun Technology Pte. Ltd.

JobOverview

Weare looking for a highly skilled Python Developer to join our team, focusing onthe engineering and deployment of AI-driven applications. In this role, youwill bridge the gap between AI models and production-ready services,specifically working with OCR (Optical Character Recognition), IV (Image/VideoUnderstanding), and TTS (Text-to-Speech) technologies. You will be responsiblefor building robust backend systems using Flask, managing complex data withMySQL and Redis, and ensuring seamless deployment via Docker on Cloud Services.A meticulous approach to code quality and performance optimization isessential.

KeyResponsibilities

-      AI Service Engineering: Design and develop scalable server-side logic to wrap and deployAI models (OCR, IV, TTS) into production-ready APIs.

-      Backend Development: Build and maintain high-performance web services using Flask, focusing onasynchronous task processing and efficient resource management.

-      Data & Workflow Management: Design and optimize MySQL schemas and leverage Redis for cachingand managing real-time message queues for AI inference tasks.

-      Media Pipeline Optimization: Implement and optimize data pipelines for processing images,videos, and audio streams to ensure low-latency AI responses.

-      Cloud & DevOps: Containerize applications using Docker and manage deployment, scaling, andmonitoring within Cloud environments (e.g., AWS, Azure, or Alibaba Cloud).

-      Code Excellence: Maintain a high standard of code quality, strictly adhering to team-specificstyle guides and engineering best practices.

Requirments

-      Work Experience: Atleast 3 years of professional experience in Python development, with a proventrack record of deploying AI or data-intensive applications.

-      Technical Stack:

Expertise in Python and Flask (experience withasynchronous frameworks like FastAPI is a plus).

Strong proficiency in MySQL (query optimization,indexing) and Redis (data structures, pub/sub).

Solid experience with Docker and CI/CD pipelines.

Familiarity with Cloud Service architectures (VPC, ObjectStorage, GPU instances).

-       AI & Media Domain Knowledge:

Hands-on experience integrating or deploying OCR, TTS, orComputer Vision models.

Knowledge of media processing libraries (e.g., FFmpeg,OpenCV, Pillow).

Strong debugging and profiling skills to optimize Pythonperformance at the system l       evel.

-      Soft Skills: Excellent logical thinking, proactive communication, and the ability to thrivein an agile, fast-paced environment.

Job Duties

-Utilize SQL to extract, clean, and process large datasets from various databases, ensuring data accuracy for risk analysis and reporting.

-Support the development and iteration of credit risk models and anti-fraud rules using machine learning algorithms such as XGBoost, GBDT, and Logistic Regression.

-Analyze financial and transactional data to identify potential risk trends, abnormal fluctuations, and fraud patterns to support decision-making.

-Perform feature engineering and data exploration to identify key risk indicators that improve model predictive performance.

-Monitor the performance of risk models and automated rules in production, assisting in troubleshooting and system stability checks.

-Collaborate with cross-functional teams, including Data Engineers and Product Managers, to translate data findings into actionable risk strategies.

Requirements

-Bachelor’s Degree or higher in a quantitative field such as Statistics, Mathematics, Computer Science, or Financial Engineering.

-Solid proficiency in SQL is a must; comfortable with complex queries and data manipulation in environments like MySQL, Hive, or Oracle.

-Proficient in Python for data analysis, with experience using libraries such as Pandas, Scikit-learn, or Matplotlib.

-Basic understanding or academic exposure to predictive modeling, including credit scorecards, classification, or regression analysis.

-Strong logical thinking and numerical reasoning skills, with a keen eye for detail and data integrity. Familiarity with statistical tools or other programming languages (e.g., R, SAS) is an advantage.

-For Junior level: Open to fresh graduates or candidates with 1–2 years of relevant experience; academic or internship exposure to predictive modeling (e.g., credit scorecards or regression analysis) is preferred.

-For Mid-level: 3–5 years of professional experience in Fintech, Banking, or Risk Control with a proven track record of deploying models into production