NEXUS CORPORATION

Machine Learning Engineer

刊登於 13-09-2026
月薪 Monthly

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We are seeking a Machine Learning Engineer to design develop and deploy machine learning solutions that support business objectives The successful candidate will work closely with data scientists data engineers and business stakeholders to build scalable AIML models and integrate them into production environments

This will be a 1 year rolling contract. Cantonese and English speaking is mandatory

Responsibilities

  • Develop train and optimize machine learning and deep learning models
  • Design and implement endtoend ML pipelines for data preparation model training deployment and monitoring
  • Collaborate with data engineers to build scalable data processing solutions
  • Deploy and maintain ML models in cloud and onpremises environments
  • Monitor model performance and implement continuous improvement processes
  • Conduct model evaluation testing and troubleshooting
  • Collaborate with business and technical teams to translate requirements into AIML solutions
  • Document technical designs methodologies and implementation details

Requirements

  • Bachelors or Masters degree in Computer Science Data Science Engineering Mathematics or a related discipline
  • 3 years of experience in machine learning model development and deployment
  • Strong programming skills in Python
  • Experience with ML frameworks such as Scikitlearn TensorFlow PyTorch or XGBoost
  • Experience building and maintaining ML pipelines and MLOps practices
  • Familiarity with cloud platforms such as Azure AWS or GCP
  • Strong understanding of data structures algorithms statistics and model evaluation techniques
  • Experience working with SQL and large datasets

Preferred Skills

  • Experience with Generative AI and Large Language Models LLMs
  • Knowledge of Docker Kubernetes and CICD practices
  • Experience with vector databases RAG architectures and AI application development
  • Exposure to financial services or regulated industries
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僱用形式
2026-10-04
薪酬
月薪 Monthly
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