Associate Software Engineer

Chubb; Kuala Lumpur; On-site; MYR 90,000 - 150,000; Full time

Job summary

Chubb is seeking a software engineer to work where AI is the default interface for design, development, testing, and delivery. All levels are expected to use AI coding assistants and agentic frameworks, while applying sound engineering judgment to validate AI outputs.

You will lead by example, shape team AI practices, mentor colleagues, and translate AI objectives into scalable, production-grade outcomes within a regulated financial services environment.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, or a related discipline.
• Hands-on proficiency with AI coding assistants and agentic development frameworks as a primary development interface.
• Practical experience with automated end-to-end testing frameworks.
• Proficiency in modern full-stack development across front-end and back-end technologies.
• Cloud development experience (Microsoft Azure preferred).
• Strong prompt engineering skills — ability to decompose ambiguous requirements into precise, effective AI task instructions.
• Proven ability to critically assess and verify AI-generated code rather than solely generate it.
• Experience integrating LLM APIs into enterprise-grade applications.
• Track record of building and shipping production applications using AI-augmented workflows with measurable outcomes.
• Experience leading technical workstreams or coaching others on AI development practices.
• Sound judgement on when AI output requires independent human review, particularly in regulated or client-facing contexts.]

Responsibilities

• Implement features and technical stories end-to-end including appropriate test coverage.
• Manage the full pull request lifecycle: branch, implementation, review response, merge.
• Write unit and integration tests for all implemented features.
• Conduct code reviews focused on correctness, security, and long-term maintainability.
• Integrate AI models and APIs with enterprise applications, ensuring scalability, security, and maintainability.
• Facilitate daily standup at senior level; keep it time-boxed, technical, and impediment-focused.
• Own squad-level technical decisions at senior level; elevate cross-squad concerns to HoE.
• Mentor peers on software engineering practices and AI tooling judgment.
• Define and champion team AI development standards — prompt engineering practices, output validation protocols, and responsible use guidelines aligned to regulatory requirements.
• Build organisational AI literacy through knowledge sharing, demos, and documentation on emerging tools and practices.
• Collaborate with QA engineers on acceptance criteria and test scenarios.
• Ensure all AI-assisted development adheres to security, data protection, and compliance standards in a regulated financial services environment.
• Use AI to assist in writing and maintaining technical documentation, API specifications, and code comments to enterprise-grade standards.
• Participate actively in sprint ceremonies, retrospectives, and team discussions.
• Support hiring at senior level: technical interviews and code assessment review

Skills

Net

Angular/React

Full-stack development

AI tooling

Testing frameworks

Education

Bachelor's degree in Computer Science or Information Technology

Tools

Azure SQL

PostgreSQL

MongoDB

Microsoft Azure

Claude AI

Playwright

GitHub

Jira

Confluence

Job description

Job Description

A software engineering role where AI is the primary mode of working — not a productivity aid but the default interface for design, development, testing, and delivery. Engineers at all levels are expected to apply AI coding assistants and agentic development frameworks as standard practice, while maintaining the engineering judgement to know when to trust, challenge, or override AI-generated outputs. Lead by example — shape team AI practices, mentor others, and translate organisational AI objectives into scalable, production-grade outcomes.

Job Description

A software engineering role where AI is the primary mode of working — not a productivity aid but the default interface for design, development, testing, and delivery. Engineers at all levels are expected to apply AI coding assistants and agentic development frameworks as standard practice, while maintaining the engineering judgement to know when to trust, challenge, or override AI-generated outputs. Lead by example — shape team AI practices, mentor others, and translate organisational AI objectives into scalable, production-grade outcomes.

Accountabilities & Responsibilities

• Implement features and technical stories end-to-end including appropriate test coverage
• Manage the full pull request lifecycle: branch, implementation, review response, merge
• Write unit and integration tests for all implemented features
• Conduct code reviews focused on correctness, security, and long-term maintainability
• Integrate AI models and APIs with enterprise applications, ensuring scalability, security, and maintainability
• Facilitate daily standup at senior level; keep it time-boxed, technical, and impediment-focused
• Own squad-level technical decisions at senior level; elevate cross-squad concerns to HoE
• Mentor peers on software engineering practices and AI tooling judgment
• Define and champion team AI development standards — prompt engineering practices, output validation protocols, and responsible use guidelines aligned to regulatory requirements
• Build organisational AI literacy through knowledge sharing, demos, and documentation on emerging tools and practices
• Collaborate with QA engineers on acceptance criteria and test scenarios
• Ensure all AI-assisted development adheres to security, data protection, and compliance standards in a regulated financial services environment
• Use AI to assist in writing and maintaining technical documentation, API specifications, and code comments to enterprise-grade standards
• Participate actively in sprint ceremonies, retrospectives, and team discussions
• Support hiring at senior level: technical interviews and code assessment review

Technical Skills

• Net, Angular/React, Azure SQL/PostgreSQL/MongoDB, MS Azure, Claude AI, Playwright, GitHub, Jira / Confluence

Skills & Qualifications

Required: Bachelor's degree in Computer Science, Information Technology, or a related discipline. Hands-on proficiency with AI coding assistants and agentic development frameworks as a primary development interface. Practical experience with automated end-to-end testing frameworks. Proficiency in modern full-stack development across front-end and back-end technologies. Cloud development experience (Microsoft Azure preferred). Strong prompt engineering skills — ability to decompose ambiguous requirements into precise, effective AI task instructions. Proven ability to critically assess and verify AI-generated code rather than solely generate it. Experience integrating LLM APIs into enterprise-grade applications. Track record of building and shipping production applications using AI-augmented workflows with measurable outcomes. Experience leading technical workstreams or coaching others on AI development practices. Sound judgement on when AI output requires independent human review, particularly in regulated or client-facing contexts.