Staff Software Engineer -AI & Middleware Automation

Visa•Austin, TX

1d•$131,600 - $210,300•Hybrid

About The Position

Visa's Middleware Product Reliability Engineering (PRE) team in Austin is hiring a Staff Software Engineer to help lead our evolution from traditional operations into a modern, AI-driven software engineering organization. This isn't about keeping the lights on — it's about building the intelligent systems that keep them on. You'll design and ship production-grade automation, mentor engineers, drive architectural decisions, and contribute to agentic AI initiatives that are actively transforming how we operate critical infrastructure. Your code will run at global scale, processing billions of transactions daily across 200+ countries. Austin is one of Visa's fastest-growing engineering hubs — and we're building a team that reflects the diversity, creativity, and ambition of this city. We value different perspectives, backgrounds, and ways of thinking. Great engineers come from everywhere.

Requirements

5 or more years of relevant work experience with a Bachelors Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD
6 or more years of work experience with a Bachelors Degree or 4 or more years of relevant experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or up to 3 years of relevant experience with a PhD
5+ years of professional software engineering experience.
Bachelor's degree in Computer Science, Software Engineering, or related field — or equivalent experience.
Proficiency in Python, Java, or Go — you write production-quality, secure, maintainable code.
Strong foundations in data structures, algorithms, systems design, and software development lifecycle.
Experience with DevOps tools and CI/CD pipelines: Jenkins, Terraform, Ansible, Docker, Kubernetes.
Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization concepts.
Working knowledge of monitoring/observability tools (Prometheus, Splunk, Grafana).
Strong debugging, troubleshooting, and root cause analysis skills.
Experience mentoring engineers and leading code reviews.
Curiosity about GenAI and agentic systems — hands-on experience preferred.
Middleware experience: Tomcat, Apache, Spring Boot, JBoss, IBM MQ, IBM DataPower, Hazelcast, Kafka, Flink, SQS.
AI/ML engineering experience or hands-on work with LLM frameworks (LangChain, LangGraph, LlamaIndex).
Experience integrating AI agents into production systems.
Secure coding practices (OWASP principles).
Open-source contributions or public engineering portfolio.

Nice To Haves

Curious and driven — you dig into the "why," take initiative, and follow through without being asked.
A systems thinker — you see how pieces connect, anticipate failure modes, and design for resilience.
A clear communicator — you explain complex concepts to any audience, technical or not.
Collaborative — you lift others up, share credit, and thrive in diverse, cross-functional teams.
Adaptable — you grow with the tech (especially AI) and bring others along.
Quality-obsessed — you use metrics to drive code quality and operational stability.

Responsibilities

Design and develop middleware reliability solutions with limited guidance — translating business and technical requirements into system designs that scale.
Produce extensible, maintainable code used across products — enforcing coding patterns, standards, and security best practices.
Lead and participate in design reviews; ensure solutions meet availability, scalability, security, and performance requirements.
Identify risks, interdependencies, and tradeoffs; define metrics to assess delivery feasibility.
Build intelligent automation systems leveraging AI/ML and LLM frameworks (LangChain, LangGraph, RAG pipelines).
Collaborate with teams across Visa to integrate AI-driven insights into reliability engineering workflows.
Identify opportunities for automation that eliminate toil and prevent recurring incidents.
Respond to complex incidents with customer or business impact — troubleshoot root causes, deploy fixes, and recommend solutions to prevent recurrence.
Participate in on-call rotations; provide incident response guidance to others.
Use advanced monitoring and observability to detect issues, identify patterns, and surface systemic risks.
Lead junior engineers in understanding requirements, coding standards, and engineering practices.
Drive code reviews that enforce quality; provide constructive technical feedback.
Teach others about tooling, automation, and reliability practices — share design learnings and investigative findings with the team.