Software Engineer (AI Cybersecurity)

About the role

Visa is seeking a Software Engineer focused on AI‑driven cybersecurity. In this role you will design, develop and enhance secure, scalable software solutions that leverage machine‑learning and generative‑AI technologies to protect our payment ecosystem.

Key responsibilities

• Develop, modify and review high‑quality, secure code in Java and Python.
• Build and integrate applications using APIs, micro‑services, messaging systems and databases.
• Apply secure coding practices such as input validation, authentication, authorization and secrets management.
• Debug production issues, perform root‑cause analysis and optimise system performance.
• Create automated unit, integration and end‑to‑end tests.
• Build and maintain CI/CD pipelines for reliable, secure deployments.
• Deploy and manage applications with containerisation and cloud‑native technologies.
• Monitor and troubleshoot production systems using observability tools.
• Leverage GenAI tools and coding co‑pilots to accelerate development and improve code quality.
• Collaborate with cross‑functional teams to translate business and cybersecurity requirements into engineering solutions.
• Maintain documentation and ensure compliance with security and regulatory standards.

Required profile

• Bachelor’s degree or at least 3 years of relevant experience (2 + years preferred).
• Knowledge of data‑science and machine‑learning concepts.
• Strong programming experience in Java and Python.
• Experience developing, debugging and testing web/backend or micro‑service applications.
• Understanding of APIs, databases and distributed systems.
• Familiarity with CI/CD pipelines and deployment workflows.
• Knowledge of secure coding practices and application‑security fundamentals.
• Experience with GenAI frameworks (LLMs, prompt engineering, RAG, agents, etc.).

Required skills

• Java
• Python
• APIs
• Micro‑services
• Messaging systems
• Databases
• Secure coding practices
• CI/CD pipelines
• Containerisation (e.g., Docker, Kubernetes)
• Cloud‑native technologies
• Observability tools
• GenAI tools and coding co‑pilots
• Large language models (LLMs)
• Prompt engineering
• Retrieval‑augmented generation (RAG)
• AI agents