Duties:
·Acquire, preprocess, and engineer features from diverse data sources.
·Design and maintain robust data pipelines while ensuring adherence to data governance standards.
·Explore, develop, and test machine learning and deep learning algorithms.
·Adapt commercial or open-source AI platforms and frameworks (e.g., Azure ML, AWS ML, Hugging Face, Deepseek, etc.) to meet organizational needs.
·Conduct thorough model validation, fine-tuning, and bias/error assessments.
·Partner with other technical teams to containerize models, expose APIs, and embed AI capabilities into HPC cloud platform using CI/CD methodologies.
·Set up monitoring tools to assess model accuracy, drift, and system performance.
·Continuously enhance models to ensure optimal performance and compliance with internal policies.
·Produce clear technical documentation and lead training sessions to empower business teams in using AI tools effectively.
·Help define best practices and contribute to internal AI guidelines and frameworks.
·Keep pace with emerging AI research and industry innovations.
·Experiment with advanced AI techniques and recommend adoption when they offer measurable business impact.
·Perform other duties assigned by supervisor.
Qualifications:
·Bachelor’s degree or higher in Computer Science, Information Technology, Artificial Intelligence, Fintech, or related fields.
·At least 3 years of practical experience in AI engineering, machine learning, or data science, ideally in corporate environments.
·Demonstrated portfolio of deployed AI models (e.g., GitHub repositories, project summaries, etc.).
·Hands-on experience with AI models and techniques (e.g. LMM, RAG, Agentic, transformer, RNN, LSTM, etc.)
·Skilled in Python, with hands-on experience using ML libraries (TensorFlow, PyTorch, scikit-learn), SQL, and cloud-based AI/ML platforms (AWS, Azure, GCP); familiarity with MLOps tools such as Docker, Kubernetes, MLflow.
·Strong proficiency in popular deep learning frameworks (e.g., TensorFlow, PyTorch, MXNet) and GPU-acceleratedprogramming (e.g., CUDA, cuDNN).
·Strong analytical mindset with solid grounding in statistics, data ethics, and governance principles.
·Excellent communication skills with the ability to distill technical concepts into actionable business insights.
·Proficient in both English and Chinese.
Job Responsibilities:
·Responsible for multi-source data collection, cleaning, pre-processing, and feature engineering
·Design and maintain a highly reliable data pipeline, strictly following data governance standards
·Develop and test machine learning and deep learning algorithms
·Deeply customize business or open-source AI platforms (such as Azure ML, AWS SageMaker, Hugging Face, Deepseek, etc.), meeting enterprise-level needs
·Execute model validation, fine-tuning, bias and error analysis
·Collaborate with backend and cloud computing teams, complete model containerization, API packaging, and integration into the HPC cloud platform through CI/CD pipelines
·Establish model accuracy, drift, and system performance monitoring mechanisms
·Continuously iterate and optimize the model to ensure performance and compliance are both optimized
·Write high-quality technical documentation and provide AI tool training to business teams
·Participate in the development of internal AI best practices and governance frameworks
·Stay up to date with global AI frontier research, evaluate and introduce innovative technologies with commercial value
·Execute other tasks assigned by the superior
Requirements:
·Computer Science, Artificial Intelligence, Fintech or related professional bachelor's degree or above
·Have a showcase of already deployed AI models (GitHub, project reports, etc.)
· Proficient in LMM, RAG, Agentic, Transformer, RNN, LSTM, etc., cutting-edge technologies
· Proficient in Python, familiar with TensorFlow, PyTorch, scikit-learn, etc. frameworks, with SQL and mainstream cloud AI platform practical experience
·Familiar with the MLOps full process tool chain (Docker, Kubernetes, MLflow, etc.)
·Have GPU acceleration programming experience (CUDA, cuDNN)
·Good statistical foundation, deep understanding of data ethics and governance principles
·Excellent technical communication skills, able to translate complex concepts into business insights
·Proficient in both Chinese and English
