Deskripsi Pekerjaan
Join Nanyang Technological University (NTU) as a Platforms Engineer, MLOps, and become a key architect in Singapore's national AI program. We are seeking a passionate technical leader to build and maintain cutting-edge AI infrastructure that empowers researchers and drives innovation. In this pivotal role, you will design scalable machine learning platforms, streamline the deployment of complex models, and establish the reliability standards required for high-stakes research.
You will act as a bridge between data science and engineering, working closely with cross-functional teams to optimize the machine learning lifecycle. By implementing robust CI/CD pipelines and state-of-the-art orchestration tools, you will ensure that our AI capabilities are accessible, secure, and performant. This is an exceptional opportunity to mentor the next generation of talent while contributing to the future of artificial intelligence in a world-class academic environment.
Tanggung Jawab
- Design, build, and maintain scalable MLOps platforms and CI/CD pipelines for machine learning workloads.
- Implement containerization strategies (Docker, Kubernetes) to ensure consistent model deployment and scalability.
- Establish comprehensive monitoring, logging, and alerting systems to ensure model reliability and performance.
- Collaborate with research teams to optimize data pipelines, feature stores, and model training workflows.
- Drive best practices in data governance, security, and compliance within the university's AI ecosystem.
- Mentor junior engineers and data scientists on MLOps tools, methodologies, and architectural patterns.
Kualifikasi
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
- Strong proficiency in Python and experience with major ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Proven experience with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes).
- Deep understanding of MLOps tools and workflows, including MLflow, Kubeflow, or Airflow.
- Experience with big data technologies (Spark, Hadoop) and distributed computing systems.
- Excellent problem-solving skills and the ability to thrive in a fast-paced, collaborative research environment.