Ngee Ann Polytechnic
AI ENGINEER
Visa Source Listed
📍🇸🇬 Singapore📅4h ago
S$66,000—S$85,200/ year
≈ ₹41L — ₹53L per year
Model Based Software DevelopmentAPI DevelopmentCertificationsArtificial IntelligenceAI ModelsTechnology ImplementationArtificial Intelligence ApplicationTechnical EngineeringSolution DevelopmentData Infrastructure
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Job Description
Summary
This is a 2-year contract position with the Digital Services & Technology Office.
The incumbent 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.
Responsibilities
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 infrastructure team.
- Implement MLOps practices including automated model training, testing, deployment pipelines, version control, and continuous integration for AI solutions.
- Deploy AI models and applications using cloud AI services (Azure, AWS) and platform infrastructure, ensuring optimal performance and resource utilisation.
- Develop APIs and microservices to serve AI models, enabling robust integration with institutional workflows and applications.
Quality Assurance & Support
- Ensure AI solutions meet NP's technical standards for scalability, reliability, security, and maintainability while complying with data privacy regulations and ethical AI guidelines.
- Provide technical support, troubleshooting, and performance optimisation for deployed AI applications and models.
- Collaborate with Infrastructure team to define requirements for AI platform capabilities and provide feedback on platform performance and functionality.
- Work closely with data scientists, product teams, and business stakeholders to align AI solutions with institutional goals and embed AI capabilities into workflows.
Requirements
- Degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related technical discipline.
- Minimum 3 years of hands-on experience in AI/ML development with demonstrated expertise in building and managing both AI infrastructure and applications in production environments.
Skills& Certifications
- Proficiency in programming languages such as Python, R, or Java, with experience in AI/MLframeworks like TensorFlow, PyTorch, scikit-learn, and related ecosystem tools.
- Experience with cloud platforms (Azure, AWS) including AI services, infrastructure-as-code, containerisation technologies (Docker, Kubernetes), and platform engineering practices.
- Expertise in MLOps tools and practices, including model lifecycle management, automated deployment pipelines, monitoring, and governance frameworks.
- Knowledge of data engineering concepts, API development, microservices architecture, and enterprise integration patterns.
- Experience with enterprise platforms such as Databricks, Microsoft Co-Pilot Studio, Power Platform, and familiarity with Singapore IT governance and security frameworks.