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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. SW Engineer - AI Security - **Company:** Visa Inc. - **Location:** Ashburn, VA, United States - **Experience:** Expert - **Salary:** $118,700.0 - $183,600.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Java (Programming Language), Test Suite, Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Audit Trail, Cloud Computing Security, Cloud Engineering, Code Review, Cyber Security, Nvidia CUDA, Continuous Integration, Data Validation, Data Structures, Software Design Patterns, Distributed Systems, Elasticsearch, Python (Programming Language), Key Management, Performance Tuning, Software Architecture, Regression Testing, Secure Coding, Software Engineering, Software Vulnerability Management, Data Logging, Flexi (Photoshop Plugin), Enterprise Software Applications, LangChain, Large Language Models, Grafana, Concurrency, Software Security, Llamaindex, Generative AI, Backend, Agentic-AI, Rate Limiting, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Low Latency, AutoGen, Machine Learning Operations, Api Design, Restful APIs, Semantic Kernel, Splunk, Docker, Golang, Programming Languages - **Published:** October 10, 2026 - **Apply:** https://diversityjobs.com/career/18604479/Sr-Sw-Engineer-Ai-Security-Virginia-Ashburn ## About the Role * 2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience * Professional software-development experience building backend services, APIs, 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., * Advanced degree (M.S. or Ph.D.) in computer science, applied math, or a related field. * Strong proficiency in Python and at least one additional language such as Java, Go, or Rust, with solid grounding in distributed-systems fundamentals - software architecture, design patterns, data structures, concurrency, API design, and performance optimization - applied to highly available RESTful, asynchronous, or event-driven services. * Hands-on experience with Generative AI application architectures, including LLM APIs, embeddings, agentic workflows, tool calling, model gateways, and orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or LlamaIndex, along with familiarity with MCP, AI-agent tools, plug-ins, or similar emerging integration patterns. * Understanding of GenAI-specific security risks (prompt injection, jailbreaking, sensitive-data exposure, insecure output handling, unauthorized tool use, 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 - threat modeling, vulnerability management, and application-security principles - applied in day-to-day engineering. * Experience developing evaluation frameworks or test suites for LLM applications, including adversarial or red-team testing, regression testing, and false-positive/false-negative analysis. * Experience with cloud-native deployment and operations - Docker, Kubernetes, CI/CD, 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 is beneficial but not required. * Ability to evaluate new technologies objectively against security, quality, latency, scalability, maintainability, and operational-readiness criteria, and to work independently on ambiguous, complex assignments. * Clear written and verbal communication skills, including the ability to explain technical and security tradeoffs to different audiences, and experience collaborating effectively in Agile, cross-functional environments spanning engineering, cybersecurity, infrastructure, research, and product. ## Description * Build and operate GenAI security systems: design, build, deploy, and maintain production services and APIs that detect and mitigate risks such as prompt injection, jailbreak attempts, sensitive-data exposure, and unauthorized or unexpected tool/agent behavior - spanning LLMs, agents, tools, APIs, MCP servers, plugins, and enterprise data sources. Apply deterministic, policy-driven controls where predictable enforcement matters, and model-based approaches where judgment is needed. * Measure and improve: build automated evaluation, testing, and feedback loops, plus production observability (logging, metrics, tracing, alerting, dashboards), so detection quality, latency, and false-positive/negative rates stay visible and auditable - then use that telemetry to close gaps. * Integrate and scale: containerize and deploy services with Docker and Kubernetes, and integrate GenAI security capabilities into enterprise applications, model gateways, developer workflows, and cloud or on-premises platforms. * Practice secure engineering: 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 clearly. * Explore and influence: evaluate emerging GenAI security technologies and frameworks through structured proofs of concept, provide technical guidance to other engineers, and help shape the team's engineering standards and practices. * Collaborate and operate: work with engineers, researchers, architects, product managers, and infrastructure teams to translate emerging AI-security risks into product capabilities, and support production releases, incident analysis, and defect resolution.