MLOps Architect

2 days ago

Hsinchu, Taiwan MediaTek Full time NT$1,800,000 - NT$2,400,000 per year

Job Description
MediaTek's IT MLOps Platform team is seeking professionals with hands-on experience in building MLOps platforms. We are dedicated to creating an enterprise-grade platform to automate the development, deployment, and operation of AI models.

Key Responsibilities

  • Plan, design, and implement the MLOps platform architecture.
  • Lead the development of core modules, such as CI/CD for ML and model training/deployment pipelines.
  • Drive MLOps standardization and process automation, and continuously optimize the platform by introducing new technologies (e.g., Kubeflow, MLflow).
  • Foster the team's technical growth and knowledge sharing.

We welcome you to join us if you are passionate about cloud/on-premise AI platform, machine learning automation, and large-scale system architecture.

Requirement
Requirements

  • Proven experience deploying MLOps architecture on cloud platforms (GCP/AWS/Azure, etc.), with strong knowledge of large-scale AI model development, training, and deployment.
  • Proficient in Python and machine learning technologies, familiar with ML/DL development frameworks (e.g., TensorFlow, PyTorch), and experienced in API development and workflow automation.
  • Practical experience in the setup and integration of MLOps platforms (e.g., Kubeflow, MLflow, SageMaker, Vertex AI).
  • Experience in CI/CD pipeline design, containerization technologies (Docker/Kubernetes), and Git version management.
  • Hands-on skills in large-scale data engineering, data flow/data versioning, model monitoring, and automated model re-training.
  • Strong project management and cross-team collaboration abilities, with independent problem analysis and solution planning skills.
  • Proactive, eager to learn and share new technologies, and committed to self-growth and pursuit of excellence.

[Preferred Qualifications]

  • Hands-on experience building/deploying MLOps platforms from scratch (Kubeflow, MLflow, SageMaker, Vertex AI, etc.).
  • Familiarity with data governance, model asset management, AI security, and compliance design/implementation.
  • Experience with AutoML, model A/B testing, drift detection, and Feature Store system setup/operations.
  • Backend API microservices development, serverless architecture design, and DevOps process integration experience.
  • Experience in large-scale data flow processing, ETL/data pipeline integration, and workflow automation development and maintenance.