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Full Stack / Software Engineer (AI Platform)

INTUIT RECRUITMENT PTE. LTD.20 COLLYER QUAY, 20 COLLYER QUAY, 049319, Singapore
ContractMid Level
$5k - $7k
per year
👁️ 0 views📝 0 applicationsPosted 7/19/2026Expires 8/18/2026

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Job Description

Our esteemed client in the IT industry is seeking a Full Stack / Software Engineer (AI Platform) to join their dynamic team: Employment Type: 36 months contract Position: Full Stack / Software Engineer Location: Central Working Hours: Monday to Friday, Office Hours Salary Package: Up to $6,900 + Bonus Job Responsibilities: Design, develop, and maintain production-grade software applications, including web applications, APIs, backend services, and frontend components.

Build AI-powered applications by integrating Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, agent frameworks, and related AI technologies.

Develop secure, scalable, and reliable software using modern engineering practices such as CI/CD, automated testing, containerisation, Infrastructure as Code (IaC), and cloud-native architectures. Build reusable frameworks, developer tools, and internal libraries to support AI application development.

Monitor, troubleshoot, and optimise deployed applications to ensure performance, scalability, and reliability. Work closely with data scientists, AI engineers, and business stakeholders to deliver AI-driven solutions.

Stay up to date with emerging software engineering and AI technologies, recommending improvements where appropriate.

Job Requirements

At least a Diploma in Computer Science, Software Engineering, Information Systems, or a related discipline. Strong programming skills in Python and experience with modern backend frameworks such as FastAPI, Flask, or Django.

Experience developing full-stack applications, including RESTful APIs, authentication, databases, and frontend integration (e. g. React). Experience developing cloud-native applications using AWS, Azure, or Google Cloud Platform (GCP). Familiarity with Docker, Kubernetes, and CI/CD pipelines.

Knowledge of AI engineering concepts such as LLMs, RAG, embeddings, vector databases, LangChain, LangGraph, or similar frameworks. Strong understanding of software architecture