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About This Role
About Us
Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.
At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.
Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.
Job Description
We started as an SRE team. Now we're becoming something more — a software engineering organization building agentic AI, automation frameworks, and self\-healing platforms that keep Visa's middleware running across 200\+ countries. If you write code that matters, think in systems, and want to shape how AI transforms infrastructure operations\- let's talk.
The Opportunity
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.
What You'll Do
Design \& Build at Scale
- 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.
Engineer the Future with Agentic AI
- 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.
Lead Production Excellence
- 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.
Mentor \& Multiply
- 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.
You'll Thrive Here If You:
- 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.
This is a hybrid position. Expectation of days in the office will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications:
- 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
Preferred Qualifications:
- 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.
- CORE TECHNOLOGIES: Python \| Java \| Go \| Agentic AI / LLMs \| MCP \| LangChain / LangGraph \| AWS / GCP / Azure \| Kubernetes \| Terraform / Ansible \| Jenkins \| Prometheus / Clickhouse / Grafana \| Flink \| Tomcat / Apache / Spring Boot \| IBM MQ \| NGINX \| NodeJS \| Connect Direct \| IBM DataPower
- WHY AUSTIN, WHY VISA, WHY NOW:
- Global impact, local community — your code runs across 200\+ countries; your team grabs breakfast tacos on South Congress.
- AI\-first engineering culture — we're deploying agentic AI in production, not just talking about it.
- Career growth — clear paths to Senior Staff, Technical Lead, or Engineering Manager.
- World\-class mentorship — learn from engineers with deep experience in distributed systems at global scale.
- Inclusive by design — we're building a team as diverse as the people Visa serves. ERGs, mentorship programs, and a culture where you belong.
- Investment in you — tuition assistance, cloud certifications, continuous learning stipends.
- Flexibility — hybrid work that respects your life outside the office.
U.S. Applicants Only
The estimated salary range for this position is $131,600\.00 to $ 210,300\.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job\-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program.Work Hours
Varies upon the needs of the department.
Travel Requirements
This position requires travel 5\-10% of the time.
Mental/Physical Requirements
This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers.
Visa is an EEO Employer
Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.
Salary Context
This $131K-$210K range is below the median for AI Software Engineer roles in our dataset (median: $190K across 193 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Visa, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $234,620 based on 682 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($170K) sits 27% below the category median. Disclosed range: $131K to $210K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Visa AI Hiring
Visa has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Highlands Ranch, CO, US, San Francisco, CA, US, Foster City, CA, US. Compensation range: $198K - $400K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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