M09 - Full Stack Engineer

What you'll do:

Develop AI and machine learning solutions by collaborating closely with cross-functional teams, including product owners, designers, data specialists, and software engineers. Contribute to the scoping, architecture, development, deployment, and integration of AI systems across cloud or on-premise environments.

• Design and develop robust, scalable, and secure full-stack applications that serve real user needs
• Write clean, maintainable, and well-tested code across frontend, backend, and infrastructure layers
• Contribute to technical design discussions, architecture reviews, and engineering best practices
• Take ownership of features from conception through deployment and ongoing maintenance
• Assess the technical feasibility of proposed AI initiatives, estimate resource requirements, and identify potential risks or challenges to make informed decisions about which AI projects to pursue and how to prioritise development efforts
• Responsible for monitoring and optimising the performance of deployed AI systems, including identifying bottlenecks, implementing efficiency improvements, and ensuring that systems can handle production workloads effectively whilst maintaining acceptable response times and resource utilisation
• Evaluate new algorithms, frameworks, and methodologies to determine their potential application within the organisation
• Support the training of URA community in the use of AI technologies
• Engage stakeholders across departments and external agencies to gather use cases and requirements, document user feedback, and support solution roll-outs with feedback loops

Requirements:

• Have a keen interest in cities and in applying expert knowledge and expertise to develop AI/ML-enabled solutions that will enable Planners to make Singapore an even more liveable, sustainable and economically competitive city.
• Bachelor's Degree or higher in Computer Science, Data Science, or related disciplines. We will also factor in relevant certifications (e.g., Coursera).

At least 2 years of relevant experience, in machine learning engineering, data science, data engineering, or other data-heavy roles, and proficiency in the following:

• Common machine learning algorithms and key parameters, Natural Language Processing, Knowledge-based Systems (KBS) and Generative AI projects.
• Developing and optimising machine learning or AI models using common Python packages such as scikit-learn, TensorFlow, or PyTorch.
• SQL and/or experience working with vector databases (such as Pinecone, Weaviate, Chroma, or similar) for similarity search, embeddings storage, and retrieval-augmented generation (RAG) applications.
• Working with cloud infrastructure and services to deploy machine learning models, pipelines, or solutions (e.g. Microsoft Azure/AWS)
• Containerization technologies such as Docker and orchestration platforms like Kubernetes, with a strong understanding of deploying and managing machine learning models in containerized environments.
• Capable of architecting machine learning or AI solutions that meet the users needs while being effective, reliable, secure, scalable, and cost-efficient.
• Capable of cleaning, imputing, and correcting anomalies in the collected structured or unstructured data to ensure high data quality standards.

Preferences:

• Understanding or experience in machine learning or AI, especially in explain ability and fairness.
• Experience in delivery of machine learning or AI solutions for internal teams or external clients