Role Overview:

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.

Responsibilities:

Develop train and optimize machine learning and deep learning models
Design and implement end-to-end 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 on-premises 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

What we are looking for:

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

Languages: Business-level proficiency in Mandarin, Cantonese, and English is mandatory.

Wymagane umiejętności

PythonScikit-learnTensorFlowPyTorchXGBoostEnd-to-end ML pipeline development and MLOps practicesCloud platforms (AWS, Azure, GCP)Containerization, orchestration and CI/CD (Docker, Kubernetes, CI/CD practices)SQL and large dataset handlingModel evaluation, testing, and monitoring (statistics and metrics)Generative AI and Large Language Models (LLMs)Vector databases and RAG architecturesScalable data processing and collaboration with data engineersTechnical documentation of designs and implementationsCross-functional collaboration and translating business requirements into AI/ML solutions

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