Senior AI Engineer for AI Singapore (AI Talent Development)

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Job Description

AI Singapore (AISG) is a national AI programme launched by the National Research Foundation (NRF) to anchor deep national capabilities in Artificial Intelligence (AI).

The programme office is hosted by the National University of Singapore (NUS) and brings together all Singapore-based research institutions and the vibrant ecosystem of AI start-ups and companies developing AI products to perform use-inspired research, grow the knowledge, create the tools, and develop the talent to power Singapore's AI efforts.

AI Singapore (AISG) in partnership with the SAF AI Centre (SAIC), is seeking an experienced AI engineer to provide mentorship and consultancy to SAIC engineering teams, support real-world project selection for skills deepening, and enable professional certification readiness.  

SAIC is a unit within the Digital and Intelligence Service, dedicated to addressing SAF's operational data science challenges and operationalising AI solutions. As the centre of excellence for AI, SAIC enhances the operational capabilities of SAF, driving productivity and efficiency through innovative AI solutions that advance SAF's defence capabilities.

This role requires security clearance.

Duties & Responsibilities 

In this role, you will:

• Develop and maintain a job-skills-proficiency (JSP) map that reflects AI state of play and keep talent development roadmap current.
• Develop and execute AI engineer certification programme, including but not limited to competency framework, assessment and qualification criteria, code of conduct, to guide AI professional development in SAF.
• Provide technical mentorship via sprints or personal mentorship; capture feedback on knowledge sharing and technical problem-solving impact.
• Identify real-world AI projects that build software engineering depth and machine learning pipeline skills of workforce for certification readiness.
• Contribute to knowledge and technical assets that uplift SAIC capability over time.

Qualifications

• Advanced degree in computer science, engineering, data science, or equivalent practical experience.
• Minimum 5 years in software engineering / ML engineering / applied AI and deploying models into production.
• Demonstrated experience in “systems thinking” across data, model, product workflow, and governance constraints (security, compliance, reliability).
• Communication skills to mentor across experience levels and translate complex topics clearly into operational goals.