V

Visa

Runs a global network for digital payments

Staff Software Engineer

Backend EngineeringConfirmed live in the last 24 hours
Full-timeSingaporeNo salary listed

About the Job

Requirements

  • At least 5 years of relevant work experience with a bachelor's degree, at least 2 years with an advanced degree such as a master's, MBA, JD, or MD, or no work experience with a PhD; alternatively, at least 8 years of relevant work experience.
  • Preferred: 6 or more years of work experience with a bachelor's degree, 4 or more years of relevant experience with an advanced degree such as a master's, MBA, JD, or MD, or up to 3 years of relevant experience with a PhD in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field.
  • Strong background in AI systems and demonstrated ability to design, build, and ship AI-powered solutions that enhance the autonomous development lifecycle and improve software quality.
  • Experience with Python and machine learning fundamentals, including data preparation and analysis, building and evaluating predictive models, and improving model performance; familiarity with regression, decision trees, random forests, and ensemble methods is beneficial.
  • Experience using Python, scikit-learn, pandas, NumPy, and Jupyter notebooks is preferred.
  • Understanding of model evaluation concepts such as accuracy, precision, and recall is helpful; deep expertise in all these areas is not required.
  • Five to eight years of hands-on software engineering experience building, delivering, and supporting enterprise-grade Java/J2EE applications, preferably using Spring Boot, RESTful APIs, microservices, and modern front-end frameworks such as Angular or React.
  • Strong foundation in software engineering principles, clean code practices, object-oriented design, data structures, algorithms, distributed systems, and scalable application architecture.
  • Hands-on experience with AI-assisted software delivery, including generative AI coding assistants and developer tools such as Claude Code CLI, GitHub Copilot, and Cline; prompt engineering; AI-supported code review, test generation, documentation, and productivity workflows.
  • Ability to validate, refactor, and secure AI-generated code with human oversight to ensure correctness, maintainability, performance, compliance, and production reliability.
  • Working knowledge of applied AI concepts relevant to software engineering, including large language models, embeddings, retrieval-augmented generation, agentic workflows, model limitations, data privacy, and responsible AI practices.
  • Knowledge of Agentic Skills, including setting up, reviewing, and maintaining skills, knowledge graphs, and related AI orchestration patterns for reliable, context-aware developer productivity workflows.
  • Demonstrated ability to design and deliver production-ready services using APIs, event-driven messaging, caching, resiliency patterns, observability, and secure integration practices.
  • Solid experience with relational databases and SQL; experience with DB2, MySQL, performance tuning, schema design, and data access optimization is preferred.
  • Experience with cloud-native engineering practices and container platforms such as Docker, Kubernetes, or OpenShift, including deployment, monitoring, scaling, and operational readiness.
  • Strong DevSecOps mindset and practical understanding of secure coding, vulnerability remediation, secrets management, dependency governance, logging, monitoring, and incident response.
  • Experience working in Agile delivery teams with ownership of stories, design decisions, engineering quality, production support, and continuous improvement.
  • Strong analytical, problem-solving, and debugging skills, including troubleshooting complex issues across application, integration, data, infrastructure, and AI-assisted delivery workflows.
  • Ability to explain technical decisions clearly, collaborate across Product, Architecture, DevOps, QA, and Security teams, and document designs and trade-offs concisely.
  • Experience in financial services, payments, dispute processing, or other high-volume, mission-critical enterprise platforms is a strong plus.

Responsibilities

  • Work independently on defined problems and features.
  • Apply established architectural patterns with good technical judgment.
  • Take ownership of code quality, testing, and production reliability.
  • Design, build, and ship AI-powered tools that enhance the autonomous development lifecycle, from code commit to production deployment.
  • Convert AI concepts into concrete, working products with clear success metrics and measurable impact.
  • Build AI systems that improve software quality through intelligent code analysis, test optimization, and automated defect detection.
  • Create AI-driven solutions that provide insights into code quality, technical debt, and system performance.
  • Deliver consistently, contribute to improving the codebase and systems, and operate with a production-first mindset.
  • Design and implement small to medium-sized features or components based on defined requirements and reference architectures.
  • Apply approved architectural patterns, including microservices, event-driven messaging, and APIs, correctly and consistently.
  • Translate functional requirements into clean, efficient, and maintainable code.
  • Translate real-world business challenges into measurable machine learning metrics.
  • Explain complex algorithmic decisions and model predictions to non-technical stakeholders.
  • Contribute to design discussions and document implementation decisions where appropriate.
  • Write readable, testable code aligned with defined engineering standards.
  • Develop and maintain automated unit and component tests.
  • Identify and address defects, performance issues, and reliability gaps in owned areas.
  • Participate in production releases and operational readiness activities.
  • Drive continuous innovation in AI-driven development through research, prototype building, and production rollouts.
  • Work closely with Product Managers, DevOps, and peer engineers.
  • Mentor junior engineers and contribute to best practices for building AI-enabled applications.

About the Company

Visa

Runs a global network for digital payments

Visa runs a global payment network that connects banks, merchants and consumers so card purchases and money transfers move between financial institutions. Its VisaNet system handles authorization, clearing and settlement, linking the bank behind a buyer's card with the bank that accepts a merchant's payment. The company earns fees from payment activity, transaction processing and international transactions, and sells fraud prevention tools, payment software and consulting services to businesses. Banks and other partners issue Visa branded cards and use its technology to build their own payment products. Visa traces its roots to a Bank of America card program launched in 1958 and is headquartered in San Francisco.

Company size
10,001+
Company stage
N/A
Total funding
$24.2M
Headquarters
San Francisco, CA
Founded
1958

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