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Visa

Runs a global network for digital payments

Senior Software Engineer - AI Security

Security Engineering & ArchitectureConfirmed live in the last 24 hours
Full-timeAshburn, VA, USA

About the Job

Requirements

  • Professional software-development experience building backend services, application programming interfaces, platforms, or distributed applications.
  • Experience developing production software using Python, Java, Go, or another modern programming language.
  • Experience with cybersecurity, secure software development, application security, cloud security, AI security, or a closely related discipline.
  • A bachelor's degree with at least two years of relevant work experience, or at least five years of relevant work experience.

Responsibilities

  • Design, build, deploy, and maintain production services and application programming interfaces that detect and mitigate risks including prompt injection, jailbreak attempts, sensitive-data exposure, and unauthorized or unexpected tool or agent behavior across large language models, agents, tools, application programming interfaces, Model Context Protocol servers, plugins, and enterprise data sources.
  • Apply deterministic, policy-driven controls for predictable enforcement and model-based approaches where judgment is needed.
  • Build automated evaluation, testing, and feedback loops, as well as production observability through logging, metrics, tracing, alerting, and dashboards; monitor detection quality, latency, and false-positive and false-negative rates, and use telemetry to address gaps.
  • Containerize and deploy services with Docker and Kubernetes, and integrate generative AI security capabilities into enterprise applications, model gateways, developer workflows, and cloud or on-premises platforms.
  • Apply threat modeling, secure coding, dependency and secrets management, code review, and vulnerability remediation; participate in architecture and design reviews and document decisions, interfaces, and operational requirements.
  • Evaluate emerging generative AI security technologies and frameworks through structured proofs of concept, provide technical guidance to other engineers, and help shape engineering standards and practices.
  • Collaborate with engineering, research, architecture, product, and infrastructure teams to translate emerging AI-security risks into product capabilities; support production releases, incident analysis, and defect resolution.
  • Own problems end-to-end, from technical design and prototyping through production deployment, monitoring, and support.

Desired Qualifications

  • Advanced degree (M.S. or Ph.D.) in computer science, applied mathematics, or a related field.
  • Strong proficiency in Python and at least one additional language such as Java, Go, or Rust, with grounding in distributed-systems fundamentals including software architecture, design patterns, data structures, concurrency, application programming interface design, and performance optimization, applied to highly available RESTful, asynchronous, or event-driven services.
  • Hands-on experience with generative AI application architectures, including large language model application programming interfaces, embeddings, agentic workflows, tool calling, model gateways, and orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or LlamaIndex; familiarity with Model Context Protocol, AI-agent tools, plugins, or similar integration patterns.
  • Understanding of generative AI security risks such as prompt injection, jailbreaking, sensitive-data exposure, insecure output handling, unauthorized tool use, and data exfiltration, and hands-on experience implementing controls such as input validation, output filtering, policy enforcement, access control, audit logging, rate limiting, and secrets management.
  • Knowledge of secure software-development lifecycle practices, including threat modeling, vulnerability management, and application-security principles, applied in day-to-day engineering.
  • Experience developing evaluation frameworks or test suites for large language model applications, including adversarial or red-team testing, regression testing, and false-positive or false-negative analysis.
  • Experience with cloud-native deployment and operations, including Docker, Kubernetes, continuous integration and continuous delivery, infrastructure automation, and observability platforms such as Splunk, Elasticsearch, or Grafana for metrics, logging, tracing, alerting, and production troubleshooting.
  • Familiarity with GPU-backed inference, CUDA, NVIDIA technologies, or model-serving frameworks.
  • Ability to evaluate technologies against security, quality, latency, scalability, maintainability, and operational-readiness criteria, and to work independently on ambiguous, complex assignments.
  • Clear written and verbal communication, ability to explain technical and security tradeoffs to different audiences, and experience collaborating in Agile, cross-functional environments spanning engineering, cybersecurity, infrastructure, research, and product.

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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