MLOps Engineer

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WhiteCoat ( https : / / whitecoat.global ) is a regional digital healthcare provider founded and headquartered in Singapore, offering on-demand telemedicine services and other services through innovation and data-driven technology. WhiteCoat’s core services include primary care tele-consultations, chronic disease management, health screening services and home-based medical services. As a digital healthcare leader, WhiteCoat partners global insurance giant, AIA, large conglomerates and other government, and financial organisations to spearhead the way for wider access to affordable healthcare across the Southeast Asia region.

As an MLOps Engineer , 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. This role is ideal for individuals with a strong understanding of both machine learning and software engineering principles who are eager to optimise the lifecycle of machine learning models.

Key Responsibilities

Design, build, and maintain scalable, secure, and reliable MLOps pipelines to streamline the deployment of machine learning and AI models and 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.

Preferred Qualifications :

Experience with model interpretability, explainability, and fairness in machine learning.

Familiarity with real-time inference systems and APIs.

Understanding of big data technologies like Spark, Hadoop, or Kafka.

Knowledge of software development best practices, including testing, version control, and agile methodologies.

Certification in cloud technologies (AWS Certified Machine Learning Specialty, Google Professional ML Engineer, etc.).

Benefits

Opportunity to shape the future of digital healthcare in Southeast Asia

Work with cutting-edge technologies in a rapidly growing company

Competitive compensation package

Professional development opportunities

What should you do next?

If you are eager to make a difference in the world of digital healthcare, we encourage you to apply!

Go to our career page at WhiteCoat Career Page , hit the apply button and we will be in touch!

Only shortlisted candidates will be contacted for further assessment.

Why Join Us?

Great & Best Place To Work!

We are A GREAT & BEST PLACE TO WORK! 92% of our employees say we are a great place to work compared to 53% of employees at a typical Global company. While people are at the center of what we do as an organisation, we can only do so if we do right by our people. And we know we can only get better by bringing in talents like you to grow together as we strive to become the healthcare provider and the employer of choice.

If you join us, you will become part of a diverse and inclusive team of passionate healthcare and technology professionals, a number that has been growing since we started in 2017. As we embark on our growth plan in the region, we will more than double our team as we move into 2025. Our team is led by some of the most experienced individuals in their respective fields.

WhiteCoat is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.