Employment Type: Full-time | Location: Singapore
Job Description

We are looking for an Software Engineer Intern (AI/Automation) to support the development of AI-driven solutions for software quality assurance. In this role, you will design and implement automation pipelines, leverage Large Language Models for requirement analysis, and integrate intelligent workflows into QA processes. This internship provides a strong foundation in applying advanced AI techniques to real-world engineering challenges, with opportunities to collaborate closely with cross-functional teams.

Responsibilities
Develop scripts to ingest and parse various document formats (e.g., Confluence pages via API, PDFs, Word documents).
Design, implement, and refine prompt strategies for Large Language Models to accurately extract requirements, user stories, and acceptance criteria from PRDs.
Build and manage an automation pipeline using frameworks like LangFlow, or by developing lightweight orchestration directly in Python.
Integrate the AI pipeline with test management tools (Zephyr Scale, Jira) to programmatically create, update, and organize test cases.
Research and experiment with prompt engineering techniques (e.g., Few-Shot, Chain-of-Thought, Retrieval-Augmented Generation) to improve accuracy and reliability.
Conduct basic validation of generated test cases against PRDs to ensure logical consistency.
Collaborate with the PM / QA team to align AI-generated test cases with existing Cucumber/Gherkin frameworks.
Document the system architecture, workflows, and best practices for maintainability.
Qualifications & Requirements
Strong programming skills in Python (or another scripting language), with a solid understanding of data structures (JSON).
Foundational knowledge of working with REST APIs.
Strong interest in AI-driven automation and building practical, innovative QA solutions.
Good written communication skills for documenting work.
Experience working with Large Language Models and prompt engineering.
Familiarity with n8n or LangFlow for AI orchestration.
Knowledge of Cucumber/Gherkin syntax and test automation concepts.
Hands-on experience with Zephyr Scale or other test management platforms.
Familiarity with version control (GitHub).
Exposure to vector databases (e.g., Pinecone, Weaviate, Milvus, ChromaDB) for embedding storage and retrieval.
Understanding of the Software Development Life Cycle (SDLC) and the role of QA in product delivery.
Prior academic, project, or internship experience in AI, automation, or QA engineering.