Assistant Principal AI Engineer, Singapore Data Science Consortium

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National University of Singapore

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Job Summary

S$5,500 - S$7,000 / Monthly EST

Job Type


Years of Experience
At least 3 years

Tech Stacks
Python SQL Typescript kafka Kubernetes Linux Prometheus ELK Podman CI Jaeger Analytics Go Docker

Job Description

National University of Singapore invites applications for the position of Assistant Principal AI Engineer in Singapore Data Science Consortium (SDSC), School of Computing. The Singapore Data Science Consortium (SDSC) was set up to deepen Singaporeโ€™s existing strengths in data science and analytics. This role supports projects from NUS Artificial Intelligence Institute (NAII) and AI & Data Analytics Strategic Technology Centre (AI.DA STC).

We seek a driven and passionate individual who can support the team in tackling complex challenges, including designing, implementing, and ensuring the delivery of end-to-end AI/ML products for clients and relevant stakeholders.

The successful incumbent will be appointed on an initial one-year contract with SDSC, NUS and subject to reappointments, based on their performance.

  •  Develop, maintain, and monitor scalable data pipelines and machine learning models across various platforms.
  •  Design and implement robust APIs to enable seamless integration and interaction of AI models with other applications.
  •  Create and manage environments for AI development and production, ensuring optimal resource allocation and compliance with security standards.
  •  Implement continuous monitoring mechanisms for AI solutions to ensure performance efficiency, accuracy, and reliability.
  •  Collaborate with cross-functional teams to implement best practices in code development, data governance, and automated pipelines.
  •  Contribute to the architecture and advancement of the data and analytics platform, exploring new tools and techniques within distributed environments.
  •  Integrate and transform data from diverse sources, such as databases, APIs, log files, and streaming platforms to support analytics and machine learning operations.
  •  Partner with stakeholders to develop solutions using Large Language Models (LLMs) tailored to business needs, ensuring the seamless integration of AI capabilities.
  •  Perform data analysis to enhance the accuracy of AI models and engage in continuous learning to keep abreast of AI/ML developments.

  • At least a Bachelorโ€™s degree with Honours in a relevant area
  • 3+ years of experience in roles that involve the intersection of AI/ML, data engineering, and/or system administration. 
  • Proven expertise in building scalable solutions. 
  • Experience with and knowledge of the following:
- Linux and Unix-based operating systems 
- Version control systems (Git) 
- Containerisation tools (Docker, podman, buildah) 
- Virtual environments/machines and dependency management 
- DevOps-related skills (CI/CD, testing, automated pipelines, packaging, etc.) 
- MLOps concepts and tooling (experiment tracking, lineage tracking, data versioning, model deployment, etc.). 
- Observability (Prometheus, Jaeger, Loki, ELK) 
- Networking concepts 
  • Proficiency and hands-on experience in Python and SQL. Familiarity with Typescript or Go would be advantageous. 
  • Experience and familiarity with distributed tooling. 
  • Ability to develop and maintain deployments/services within a Kubernetes environment. Familiarity with tools relevant to the Kubernetes ecosystem is expected. 
  • Experience with batch data processing and data modeling. Familiarity with real-time implementations would be advantageous. 
  • Understanding and awareness of software engineering and data science best practices. 
  • Understanding machine learning concepts, including NLP, computer vision, and reinforcement learning. 
  • Excellent analytical, problem-solving, and communication skills. 
  • Familiar with Scrum methodology and agile practices is preferred
  • Experience with streaming data technologies such as Kafka is preferred
  • Exposure to LLM and Generative AI-centric tools, including, but not limited to, the Hugging Face ecosystem, Ollama, and vLLM, and experience fine-tuning LLMs is preferred

Only shortlisted candidates will be notified.

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