Digital Application Developer

About the role

AECOM is seeking a Digital Application Developer to design, build, test and maintain innovative software and AI‑powered solutions that meet client needs. You will work closely with cross‑functional teams to translate business requirements into reliable, secure digital products.

Key responsibilities

• Design, develop, test, and maintain web‑based software systems and AI applications.
• Collaborate with stakeholders to convert business needs into technical specifications.
• Build and manage system infrastructure, including data pipelines, storage, compute environments, and AI model integration frameworks.
• Conduct functional, performance, and security testing to ensure reliability.
• Provide technical support and troubleshooting for deployed solutions.
• Document processes, workflows, and system architecture for knowledge sharing.
• Stay current on programming advances, Generative AI, LLM technologies, and related trends.
• Propose and implement improvements for system efficiency and performance.
• Ensure compliance with security standards, data‑privacy laws, and industry regulations.
• Work closely with cross‑functional teams to deliver innovative solutions.

Required profile

• Bachelor’s degree in Computer Science, Software Technology or a related field (professional qualifications a plus).
• Minimum 3 years of relevant IT experience (e.g., analyst programmer, web developer, system support) within the past 10 years.
• Strong knowledge of SQL databases; familiarity with NoSQL or other database types is advantageous.
• Proficiency in Python and JavaScript.
• Broad understanding of DevOps practices, containerisation (Docker, Kubernetes) and automated testing/deployment.
• Hands‑on experience with AI technologies such as LLMs, AI chatbots, AI agents, prompt engineering, vector databases, or Retrieval‑Augmented Generation.
• Native fluency in spoken Cantonese and proficiency in written Chinese.

Required skills

• Python
• JavaScript
• SQL
• NoSQL databases
• Docker
• Kubernetes
• DevOps practices
• AI technologies (LLMs, AI chatbots, AI agents, prompt engineering, vector databases, Retrieval‑Augmented Generation)
• Web‑based solution development
• Database implementation
• AI application development and deployment