AI Engineer Jobs in Austin, TX

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

Software Development Engineers are expert problem-solvers and builders who design, implement, and improve software applications and systems. In this role, engineers spend approximately 60–75% of their time coding, working hands-on with code, data, and modern tools (including AI-assisted development, cloud services, and automation frameworks) to deliver secure, scalable, and high-quality technology solutions that drive business outcomes in the fintech sector. They collaborate with cross-functional teams – product managers, designers, data scientists, QA, operations, and compliance – to translate business requirements into robust technical solutions, all while adhering to best practices, security standards, and regulatory requirements.

All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools (e.g. ChatGPT, Microsoft Copilot) to support everyday work.

This role sits within an AI-first engineering team building production-grade agentic systems powered by large language models (LLMs). Engineers design and implement multi-agent orchestration frameworks, retrieval-augmented generation (RAG) pipelines, tool-calling integrations, and LLM inference services — all at enterprise scale on on-premise infrastructure.

Key Responsibilities:

• Collaborate with stakeholders to interpret requirements for project components and incorporate feedback into future designs or solution fixes.
• Translate business requirements by verifying assumptions and escalating potential design issues to appropriate stakeholders.
• Participate in system design and architecture, refining code plans and contributing to design documentation.
• Contribute to project estimation and escalate issues that may cause delays.
• Develop, implement, and maintain code for products, services, or components, applying coding patterns, guidelines, and best practices.
• Apply debugging tools to verify assumptions and proactively flag issues before they occur.
• Participate in code reviews to ensure coding standards are followed and address routine pull requests.
• Create test plans and configure testing procedures to identify and resolve defects for multiple features.
• Respond to support requests during on-call rotations, troubleshooting issues and deploying fixes under guidance.
• Leverage and build knowledge of software developer tools to create, debug, and maintain code for components.
• Remain current in skills by investing time in training resources to improve product availability, reliability, efficiency, observability, and performance.

AI & Agentic Systems

• Design, implement, and tune multi-agent LLM workflows using agent orchestration frameworks (e.g., LangGraph, LangGraph4j, LangChain4j), including declarative graph compilation, conditional routing between agent nodes, state persistence, and dynamic agent spawning at runtime.
Build and maintain ReAct (Reasoning + Acting) agent loops — iterative Reason Act
• Observe cycles in which an LLM selects tools, interprets tool results, and continues until a terminal answer is produced.
• Implement Supervisor/Orchestrator agent patterns where a Lead Agent dynamically plans, spawns, and coordinates Specialist Sub-Agents using conditional graph edges and tool frameworks.
• Develop MCP (Model Context Protocol) tool servers that expose enterprise data sources as callable tools for AI agents, enabling natural-language-driven access to structured backend systems.
• Implement and refine RAG (Retrieval-Augmented Generation) pipelines: document ingestion and chunking (PDF/Word), embedding generation, vector storage (e.g., PostgreSQL + pgvector), and semantic similarity retrieval to ground LLM responses in authoritative source documents.
• Integrate with enterprise LLM inference gateways via REST APIs, including request shaping, prompt template management, token budget control, and graceful degradation under service unavailability.
• Design and implement agent memory architectures — both short-term per-agent isolated context stores and long-term shared knowledge repositories used across agent workflows.
• Build confidence scoring and uncertainty quantification mechanisms for AI-generated outputs, including threshold-based routing of low-confidence results to human review.
• Implement agent feedback loops — capturing human reviewer decisions and feeding structured corrections back into prompt templates and knowledge repositories to continuously improve model behavior.
• Write LangChain4j / LangChain tool definitions (e.g., Java @Tool-annotated methods) and wire them into ReAct agents for data transformation, external API calls, and domain-specific processing.
• Build and maintain agentic audit trails — structured logs capturing every tool call, agent decision, LLM prompt/response, and state transition for traceability and explainability.
• Develop and test Spring Boot microservices (Java 21, Spring Boot 3.5.x) that host AI orchestration engines, expose REST APIs for downstream consumers, and integrate with upstream data source systems.
• Author and maintain Helm charts and Jenkins CI/CD pipelines for containerized deployment of AI services to Kubernetes/OpenShift clusters across multi-region on-premise data centers.
• Build end-to-end and integration tests for non-deterministic AI components — including prompt regression suites, tool call mock frameworks, agent output schema validators, and determinism gates to catch silent prompt drift before production.

Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.

Qualifications

Basic Qualifications:

• 2 or more years of work experience with a Bachelor’s Degree or an Advanced Degree (e.g. Masters, MBA, JD, MD, or PhD)

Preferred Qualifications:

• 3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)
• 2+ years of relevant work experience and a Bachelor's degree, OR 5+ years of relevant work experience.
• 3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD).
• Experience in technologies/software systems or a directly related field (minimum two years).
• Experience in developing and/or implementing web-based or mobile applications (minimum two years).
• Experience in system design and architecture for product components.
• Experience in debugging and troubleshooting software issues.
• Experience in code review and applying coding standards.
• Experience in test planning and execution for software features.
• Experience in responding to support requests and deploying fixes.
• Experience in using software developer tools for code creation and maintenance.
• Experience developing backend services in Java (Java 17+ preferred; Java 21 a strong plus).
• Familiarity with REST API design, Spring Boot, and JPA/ORM-based data access patterns.
• Foundational understanding of LLM APIs — prompt construction, token limits, and response parsing.
• Experience building and testing enterprise-scale web services (minimum one year).
• Experience working on client-facing project or technical teams (minimum one year).
• Experience in integrating feedback into design and solution fixes.
• Experience in mentoring junior engineers and collaborating with cross-functional teams.
• Hands-on experience building agentic AI systems using LangGraph, LangChain, LangGraph4j, or LangChain4j — specifically multi-agent graph construction, node/edge definitions, conditional routing, and state schema design.
Experience implementing the React prompting pattern (Thought Action
• Observation) in a production or near-production LLM application.
• Working knowledge of Model Context Protocol (MCP) — server registration, tool schema definition (tools/list, tools/call), and client-side integration.
• Experience building RAG pipelines: document chunking strategies, embedding models, vector database querying (pgvector, Pinecone, Weaviate, or equivalent), and retrieval relevance tuning.
• Experience with prompt engineering — including structured output enforcement (JSON schema), chain-of-thought prompting, few-shot example design, and system prompt management.
• Experience building and monitoring LLM observability — tracking token usage, latency per agent step, tool call success rates, and output quality metrics in production.
• Experience with PostgreSQL including the pgvector extension for embedding storage and similarity search.
• Experience deploying containerized workloads on Kubernetes or OpenShift, including Helm chart authoring, rolling deployments, and health probes.
• Experience designing audit logging for AI agent decisions — capturing inputs, reasoning traces, tool calls, and outputs in a structured, queryable format.
• Familiarity with Aspect-Oriented Programming (AOP) for cross-cutting concerns such as agent call logging, latency measurement, and security enforcement in Spring Boot.
• Experience with CI/CD pipelines (Jenkins or equivalent) for AI/ML service deployments, including version gating and environment promotion strategies.

Information for US Applicants

For roles located in the US, the estimated salary range for this position is $110,700.00 to $ 171,800.00 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.