Software Engineering Intern

Location: Singapore Remote-friendly Eligibility: Undergraduate students currently in their 2nd or 3rd year (Sophomore or Junior) About the Role We build AI-native software products designed to solve real business problems. This is not a traditional internship where you work on isolated tasks. You will partner directly with business stakeholders, take ownership of projects from idea to delivery, and help build practical AI-powered tools and workflows used by real users. We are looking for builders — people who enjoy turning ideas into working products and are excited about applying AI to real-world challenges. What You’ll Do Own projects end-to-end: scope, build, test, iterate, and ship Work directly with business stakeholders to translate requirements into software solutions Design and implement AI-powered workflows, tools, and automations Build and integrate applications using LLMs, APIs, agents, and AI frameworks Evaluate prompts, models, and AI outputs to improve reliability and usability Document solutions and continuously improve them based on feedback Requirements Currently pursuing a Bachelor’s degree in Computer Science, Software Engineering, AI, or a related field Undergraduate student in Year 2 or Year 3 (Sophomore or Junior preferred) Strong Python programming skills Experience building or experimenting with LLM-based applications, AI agents, RAG systems, automations, or AI workflows Able to work independently and drive tasks to completion with limited supervision Strong problem-solving ability and willingness to learn quickly Good written and verbal English communication skills Nice to Have Experience shipping personal projects, hackathon projects, or open-source contributions Familiarity with Git, GitHub, and modern software development workflows Experience with AI tools and frameworks such as OpenAI APIs, Claude, LangChain, CrewAI, AutoGen, or similar Exposure to cloud platforms and deployment tools Interest in finance, operations, SaaS, or business automation What You’ll Learn End-to-end project ownership: from requirements to deployment Practical AI engineering: tool selection, prompt design, evaluation, and iteration How to work effectively at the intersection of business and engineering Building and shipping AI-powered software in a production context