Ai Lead, Office of Integrated Information Technology Services

**COMPANY DESCRIPTION**

Singapore Management University is a place where high-level professionalism blends together with a healthy informality. The 'family-like' atmosphere among the SMU community fosters a culture where employees work, plan, organise and play together building a strong collegiality and morale within the university.

Our commitment to attract and retain talent is ongoing. We offer attractive benefits and welfare, competitive compensation packages, and generous professional development opportunities all to meet the work-life needs of our staff. No wonder, then, that SMU continues to be given numerous awards and recognition for its human resource excellence.

**RESPONSIBILITIES**
- Innovation Enabled
- Modernize IT Services: Leverage AI technologies to automate workflows and modernize IT operations, ensuring they are secure, resilient, and efficient, contributing to operational excellence.
- User-Centric
- Enhance User Experience with AI: Implement AI-driven tools and services that provide seamless and consistent experiences across SMU's digital platforms, grounded in human-centered design principles.
- Data-Driven
- Build Unified Data Solutions: Collaborate with the data team to create AI models and pipelines that unify and make institutional data accessible for informed decision-making and analysis.
- Generate Intelligent Insights: Use advanced analytics and AI techniques to deliver actionable insights, helping stakeholders identify opportunities for improvement and refine services.
- Technical Execution and Collaboration
- AI Model Development: Develop and optimize machine learning models, ensuring scalability, accuracy, and relevance to SMU's needs.
- Infrastructure and Tools: Set up and manage the necessary infrastructure, tools, and pipelines to support AI development and deployment efficiently.
- Cross-Functional Collaboration: Work with stakeholders across departments to understand requirements, integrate solutions, and ensure AI systems meet business needs.
- Governance and Continuous Improvement
- Ensure Ethical AI Practices: Work with the governance team to implement governance frameworks and maintain compliance with data privacy and security policies in all AI initiatives.
- Upskill and Support: Provide hands-on technical training to team members and SMU staff, building internal AI capabilities and fostering a culture of innovation.

**QUALIFICATIONS**
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related technical field.
- Minimum 8 years' experience in an IT role, with 2 to 5 years in AI and related fields.
- Professional Certifications (optional but desirable):

- Certifications in AI and Machine Learning, such as:

- Microsoft Certified: Azure AI Engineer Associate
- Google Professional Machine Learning Engineer
- AWS Certified Machine Learning Specialty
- Data-related certifications, such as:

- Certified Data Scientist or similar credentials.
- AI ethics or governance certifications are an added advantage.
- Hands-on experience in deploying AI/ML models on cloud platforms, e.g. Azure Cognitive Services, AWS SageMaker, or Google AI Platform.
- Familiarity with cloud-native tools for orchestration, such as Kubernetes, and serverless computing solutions like AWS Lambda or Azure Functions.
- Knowledge of cost optimisation strategies for AI workloads in the cloud.
- Good knowledge of Python or other programming languages for machine learning and data science tasks, including libraries like NumPy, pandas, and Matplotlib.
- Ability to write efficient code for statistical modeling or production-grade AI system development.
- Familiarity with shell scripting and version control tools like Git.
- Proficiency in building, training, and deploying machine learning models using frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras.
- Strong understanding of advanced ML concepts like transfer learning, reinforcement learning, and unsupervised learning.
- Expertise in working with large-scale data using tools like Apache Spark, Hadoop, or SQL.
- Familiarity with ETL (Extract, Transform, Load) processes to prepare and clean data for machine learning.
- Understanding of database systems, both relational (e.g., MySQL, PostgreSQL) and non-relational (e.g., MongoDB, Cassandra).
- Proven track record of designing, implementing, and maintaining AI/ML models that solve real-world problems.
- Experience in developing AI pipelines for model training, testing, and deployment.
- Knowledge of MLOps practices for scalable and maintainable AI systems.
- Demonstrated ability to work effectively in cross-functional teams, engaging with stakeholders to understand business requirements and translate them into technical solutions.
- Experience in a university setting, contributing to academic or research projects, or supporting faculty and students with technical AI solutions is an advantage.
- Strong communication skills to articulate technica