Intern - Software Engineering (Spring 2027, Jan to Jun 2027)

Short overview on the Unit/Team:

Our team builds AI-powered internal tools that help SGX engineering teams work smarter and faster. Our current initiatives include an AI SRE assistant that intelligently analyses production logs, metrics and alerts to speed up incident detection and root-cause analysis, and a Quality Centre that uses AI to analyse test results, defects and code quality signals to give teams a clear, real-time view of software quality across our systems.

Learning Outcomes

The selected intern will gain hands-on experience building production-grade AI applications for a real engineering organisation. The intern will learn how to:

• Apply large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG) and agentic workflows to real operational problems such as log analysis and quality reporting.
• Work with observability and quality data (application logs, metrics, alerts, test results, defect records) and turn it into actionable insights.
• Design, build and ship full-stack tools (backend services, APIs, data pipelines and simple web UIs) following modern software engineering practices such as version control, code review, automated testing and CI/CD.
• Evaluate and measure the accuracy and usefulness of AI features, and iterate based on feedback from engineering and SRE users.
• Understand how a financial market infrastructure operates its technology, including site reliability engineering (SRE) practices and software quality management.

Job Description

• Assist in developing the AI SRE assistant: build pipelines that ingest and pre-process logs, metrics and alerts, and use LLMs to summarise incidents, detect anomalies and suggest likely root causes.
• Assist in developing the Quality Centre: aggregate data from test automation, defect tracking and code analysis tools, and build AI-driven analysis and dashboards that summarise quality status and highlight risk areas.
• Design and refine prompts, RAG knowledge bases and evaluation datasets to improve the accuracy and reliability of AI outputs.
• Build backend services, APIs and integrations (e.g. with logging, monitoring, CI/CD and issue-tracking platforms) that power these tools.
• Write unit and integration tests, and participate in code reviews and team sprint ceremonies.
• Gather feedback from engineering and SRE users, document findings and propose improvements to the tools.

Knowledge and Skill Requirements (please indicate if it is a ‘must’)

• MUST be pursuing a degree in Computer Science, Software Engineering, Information Systems, Data Science or a related discipline.
• MUST have good programming skills in Python or another modern language such as Java, TypeScript or Go.
• MUST have a basic understanding of large language models and interest in applying AI to engineering problems.
• Familiarity with web development (REST APIs, a backend framework and a frontend framework such as React) is an advantage.
• Familiarity with SQL, data processing and basic data analysis is an advantage.
• Exposure to logging/monitoring tools (e.g. Splunk, ELK, Grafana, Prometheus), CI/CD or test automation is an advantage.
• Familiarity with LLM frameworks or tooling (e.g. LangChain, LlamaIndex, vector databases, Claude/OpenAI APIs) is an advantage.
• Strong analytical and problem-solving skills, detail oriented, and a willingness to learn and experiment.
• Good command of English and the ability to communicate technical ideas clearly.