AI-Assisted Software Engineer

Join Razer's fintech technology team in Nanshan, Shenzhen as an AI-Assisted Software Engineer.

We are looking for engineers who embrace an AI-first development mindset and can effectively use AI coding tools to accelerate the entire software development lifecycle.

Key Responsibilities

AI-Assisted Software Development

• Use AI coding agents and development tools to improve software development productivity and quality.
• Apply AI throughout the development lifecycle, including requirement analysis, technical planning, solution and architecture design, code generation, code refactoring, code review, unit and integration testing, debugging, documentation, deployment preparation, and production troubleshooting.
• Convert business requirements into clear technical implementation plans.
• Review, validate, and improve AI-generated code before production use.
• Understand when to use AI automation and when human engineering judgement is required.
• Continuously explore new AI engineering tools, workflows, and development practices.

Fintech Product Development

• Design, develop, and maintain secure and scalable fintech applications and services.
• Participate in development of systems such as payment gateway, payment APIs, merchant integration, card payments, QR payments, e-wallet payments, bank transfers, refunds, settlement and reconciliation, payout and disbursement, and merchant management.
• Integrate with banks, payment channels, card networks, and third-party service providers.
• Develop secure APIs, backend services, and transaction-processing components.
• Ensure payment transactions are reliable, traceable, and protected against duplicate processing.

Software Engineering

• Write clean, maintainable, secure, and production-ready code.
• Participate in system design and architecture discussions.
• Develop REST APIs, microservices, event-driven services, and backend applications.
• Apply appropriate software engineering principles and design patterns.
• Implement proper error handling, retry mechanisms, idempotency, timeout management, rate limiting, logging, and monitoring.
• Perform code reviews and contribute to engineering standards.
• Identify and reduce technical debt.

Testing and Quality Engineering

• Use AI to assist in generating and improving test cases.
• Develop unit, integration, and automated tests.
• Participate in functional, regression, performance, and security testing.
• Ensure critical payment flows have sufficient automated test coverage.
• Investigate defects and identify root causes rather than applying temporary fixes.
• Improve software quality through continuous testing and automation.

DevOps and Deployment

• Participate in CI/CD and automated software delivery.
• Deploy and maintain applications in cloud and container environments.
• Work with Docker, Kubernetes, and modern deployment platforms where applicable.
• Support automated deployment, canary deployment, blue-green deployment, rollback, environment configuration, and work closely with DevOps/SRE teams to improve deployment reliability.
• Participate in production monitoring and incident troubleshooting.

AI-First Engineering Expectations

• Candidates should be comfortable treating AI as an engineering partner rather than simply a search or autocomplete tool.
• You should be able to break complex engineering problems into tasks suitable for AI agents, provide clear technical context and instructions to AI coding tools, use AI agents to inspect and understand unfamiliar codebases, generate implementation plans before coding, ask AI to propose multiple technical approaches and evaluate their trade-offs, use AI to generate tests before or alongside implementation, use AI for debugging, log analysis, and root-cause investigation, review AI-generated code for correctness, security, and maintainability, detect AI hallucinations and technically incorrect recommendations, and prevent sensitive company and payment information from being exposed to unauthorized AI services.

Security and Fintech Requirements

• Follow secure software development practices.
• Understand common application and API security risks.
• Protect sensitive information, credentials, API keys, and payment data.
• Apply appropriate authentication and authorization controls.
• Understand encryption, tokenization, and secure data handling concepts.
• Follow company security and change-management policies.
• Familiarity with PCI DSS and payment industry security requirements is highly desirable.
• Experience developing systems within regulated or security-sensitive environments is an advantage.

Technical Requirements

• Candidates should have practical experience with several of the following: Java, PHP, Go, Python, Node.js, C#, RESTful APIs, microservices, SQL and relational databases, MySQL, PostgreSQL, MariaDB, Redis, Kafka, RabbitMQ, Git, Linux environments, Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS, Alibaba Cloud, Tencent Cloud, Huawei Cloud, or Azure.
• Strong fundamentals are more important than experience with any single programming language.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or related discipline.
• Approximately 3+ years of professional software development experience; exceptional candidates with less experience may also be considered.
• Strong programming and problem-solving fundamentals.
• Practical experience using AI coding assistants or AI development agents.
• Ability to independently analyze requirements and deliver software solutions.
• Good understanding of databases, APIs, and backend system development.
• Experience with fintech, payments, banking, or financial systems is strongly preferred.

Preferred Qualifications

• Candidates with the following experience will have an advantage: payment gateway or payment-processing systems, high-volume transaction platforms, financial reconciliation or settlement systems, PCI DSS environments, high-availability architectures, cloud-native applications, microservices and distributed systems, DevSecOps, automated testing, AI agent workflows, AI-assisted code review or testing, and production incident troubleshooting.