Machine Learning Engineer (MLE)機器學習工程師

Job Requirements

As a Machine Learning Engineer (MLE), you will be responsible for adapting and deploying group-level machine learning solutions across business units on cloud platforms. Your role encompasses enhancing R&D code into production-grade pipelines, setting up APIs, ensuring CI/CD compliance, and collaborating with infrastructure teams to achieve operational readiness.

Key Responsibilities

• Production Deployment:

  • Hands-on experience deploying ML models in cloud environments (AWS/GCP/Azure)

  • Proficient in containerization (Docker) and orchestration frameworks (Kubernetes, ECS)

• ML Pipeline Development:

  • Ability to build and optimize ML pipelines using existing codebases/templates

  • Customize configurations to meet business-specific requirements

• Engineering Best Practices:

  • Strong understanding of CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions)

  • Experience with MLOps tools (MLflow, Kubeflow, TFX) for model versioning and monitoring

• Collaboration & Scalability:

  • Work closely with Data Scientists to operationalize models

  • Ensure scalable, low-latency API deployments (FastAPI, Flask)

  • Collaborate with Infrastructure/DevOps teams on resource optimization

Preferred Qualifications:

• More than 3 years of relevant experience, be able to communicate proficiently in English

• Experience with distributed training (Horovod, Ray)

• Knowledge of model optimization (quantization, pruning) for edge deployment

• Background in NLP, computer vision, or recommendation systems