Digital Healthcare Innovator

3 days ago

Kuala Lumpur, Kuala Lumpur, Malaysia WhiteCoat Global Full time

Company Overview

WhiteCoat Global is a digital healthcare provider that leverages innovation and data-driven technology to deliver on-demand telemedicine services and other healthcare solutions in the Southeast Asia region. We partner with global insurance giants, large conglomerates, and government organizations to make affordable healthcare accessible across the region.

Job Description

As a Digital Healthcare Innovator, you will be responsible for developing and maintaining the infrastructure and tools required to deploy, monitor, and manage machine learning models in production environments. You will collaborate closely with data scientists, data engineers, software engineers, and DevOps teams to ensure seamless integration and operation of machine learning pipelines.

Key Responsibilities

  • Design, build, and maintain scalable, secure, and reliable MLOps pipelines to streamline the deployment of machine learning and AI models into production.
  • Implement monitoring and logging frameworks to track model performance, detect drift, and ensure reliability over time.
  • Develop and maintain CI/CD pipelines for machine learning workflows, ensuring automated testing, validation, and deployment.
  • Monitor the performance of AI models in production, detect data or model drift, and take corrective actions to maintain relevance.
  • Collaborate with data scientists to streamline feature engineering, transform prototypes into robust, production-ready systems, and optimise model training and inference processes.
  • Optimise infrastructure costs and performance for training and deploying models, leveraging cloud platforms (e.g., AWS / GCP) and containerisation tools like Docker and Kubernetes.
  • Establish robust data versioning, experiment tracking, and reproducibility practices.
  • Ensure compliance with data privacy regulations and implement security measures to protect models and sensitive data.
  • Troubleshoot and resolve production issues related to model performance and deployment.
  • Fine-tune system performance to optimise latency and throughput for real-time applications.
  • Identify opportunities to minimise operational costs while maintaining the effectiveness of AI solutions.
  • Stay updated with emerging trends and best practices in MLOps, machine learning, and cloud-native development.

Requirements

  • Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
  • 3+ years of experience in MLOps, DevOps, or a related engineering role.
  • Proficiency in programming languages such as Python, Java, or Scala, with a focus on machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
  • Strong understanding of containerisation and orchestration tools, such as Docker and Kubernetes.
  • Hands-on experience with cloud platforms (AWS, Azure, GCP) and their machine learning services.
  • Familiarity with tools like MLflow, Kubeflow, Airflow, or similar for workflow orchestration and experiment tracking.
  • Solid knowledge of CI/CD practices and tools such as Jenkins, GitLab CI/CD, or GitHub Actions.
  • Experience with data versioning and management tools (e.g., DVC, Delta Lake, or similar).
  • Strong problem-solving skills and the ability to troubleshoot production issues effectively.
  • Excellent communication and collaboration skills to work across interdisciplinary teams.

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