AI ENGINEER
AIENGINEER-DIGITALSERVICES&TECHNOLOGYOFFICE
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“AI ENGINEER - DIGITAL SERVICES & TECHNOLOGY OFFICE at AI ENGINEER. Skills: AI/ML development, GenAI models, Cloud AI services, MLOps practices. Develop production-ready AI applications. Develop production-ready AI systems”
What You'll Achieve.
Improve NP operations through intelligent automation; Improve NP operations through advanced analytics; Transition AI models from research to production; Embed AI capabilities into NP's administrative workflows; Embed AI capabilities into NP's academic support workflows; Ensure AI solutions meet NP's technical standards; Comply with data privacy regulations; Comply with ethical AI guidelines; Align AI solutions with institutional goals
Industry & Context.
Problem-solver with a performance-driven mindset
What They're Looking For.
Must Have
Minimum 3 years of hands-on experience in AI/ML development, Demonstrated expertise in building and managing both AI infrastructure and applications in production environments
Nice to Have
Familiarity with Singapore IT governance and security frameworks
What You'll Do.
Develop production-ready AI applications
Develop production-ready AI systems
Build robust AI solutions
Build scalable AI solutions
Implement AI/ML models
Design GenAI-powered applications
Develop GenAI-powered applications
Implement GenAI-powered applications
Integrate AI solutions
Build machine learning models
Train machine learning models
Build deep learning models
Train deep learning models
Build neural network models
Train neural network models
Configure AI development environments
Utilise AI development tools
Implement MLOps practices
Automate model training
Automate model testing
Automate deployment pipelines
Deploy AI applications
Develop microservices
Ensure AI solutions meet standards
Provide technical support
Support deployed AI applications
Support deployed AI models
Define requirements for AI platform
Provide feedback on platform performance
Provide feedback on platform functionality
Align AI solutions with goals
Embed AI capabilities into workflows
How You'll Work.
Team & Collaboration
Work in close collaboration with AI Product Manager; Work in close collaboration with data scientists; Work in close collaboration with cross-functional teams; Collaborate with Infrastructure team; Work closely with data scientists; Work closely with product teams; Work closely with business stakeholders; Collaborate within cross-functional teams
Communication Scope
Communicator able to work across AI, governance, and business stakeholders
Full Job Description
**[What the role is]** This is a 2-year contract position with the Digital Services & Technology Office. The AI Engineer will be instrumental in implementing NP's AI transformation strategy by developing production-ready AI applications and systems that leverage AI tools and Cloud AI services. This role focuses on building robust, scalable AI solutions using GenAI models, deep learning, neural networks, and other AI technologies to improve NP operations through intelligent automation and advanced analytics. Working in close collaboration with the AI Product Manager, data scientists, and cross-functional teams, the AI Engineer will transition AI models from research to production and embed AI capabilities into NP's administrative and academic support workflows. **[What you will be working on]** **AI Solution Development** * Design, develop, and implement AI/ML models, algorithms, and GenAI-powered applications (including conversational AI, content generation, and intelligent document processing systems) using diverse approaches and platforms such as Microsoft Copilot Studio, Databricks Apps, Azure AI Studio, and custom development frameworks. * Deploy and integrate AI solutions with existing enterprise systems, ensuring compatibility with data infrastructure and institutional workflows. **Model Development & Operations** * Design, build, and train machine learning, deep learning, and neural network models tailored to address specific institutional needs across administrative and operational domains. * Collect, analyse, and clean data from various sources to train and test AI models, working closely with data engineering teams to ensure data quality and accessibility. * Test, validate, and optimise algorithms to ensure reliability, scalability, and performance across various use cases and production environments **Technical Implementation** * Configure and utilise AI development environments and tools within the AI platform infrastructure provided by the infrastructur
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