V

Visa

Runs a global network for digital payments

Senior Software Engineer AI

Full-TimeUpdated on 10/23/2026
$110.7k - $171.8k/yr+ Potential sales incentives + Bonus + Equity
Mid
Bachelor's, Master's, MBA, PhD, JD, MD
Austin, TX, USA
HybridAt least 3 days per week in the office; travel 5–10%.
Software EngineeringAI Application Engineering, Backend Engineering

About the job

Requirements
  • At least 2 years of work experience with a Bachelor’s degree or an advanced degree.
  • Experience in technologies, software systems, or a directly related field (minimum 2 years).
  • Experience developing and/or implementing web-based or mobile applications (minimum 2 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 responding to support requests and deploying fixes.
  • Experience using software developer tools for code creation and maintenance.
  • Experience developing backend services in Java; Java 17+ preferred and Java 21 a strong plus.
  • Familiarity with REST API design, Spring Boot, and JPA/ORM-based data access patterns.
  • Foundational understanding of LLM APIs, including prompt construction, token limits, and response parsing.
  • Experience building and testing enterprise-scale web services (minimum 1 year).
  • Experience working on client-facing projects or technical teams (minimum 1 year).
  • Experience integrating feedback into designs and solution fixes.
  • Experience 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), including server registration, tool schema definition (tools/list, tools/call), and client-side integration.
  • Experience building retrieval-augmented generation (RAG) pipelines, including 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, including tracking token usage, latency per agent step, tool call success rates, and production output quality metrics.
  • 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.
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, refine code plans, and contribute to design documentation.
  • Contribute to project estimation and escalate issues that may cause delays.
  • Develop, implement, and maintain code for products, services, or components while applying coding patterns, guidelines, and best practices.
  • Use debugging tools to verify assumptions and proactively flag issues.
  • Participate in code reviews, ensure coding standards are followed, and address routine pull requests.
  • Create test plans and configure testing procedures to identify and resolve defects across multiple features.
  • Respond to support requests during on-call rotations, troubleshoot issues, and deploy fixes under guidance.
  • Use and build knowledge of software developer tools to create, debug, and maintain code for components.
  • Invest time in training to improve product availability, reliability, efficiency, observability, and performance.
  • Design, implement, and tune multi-agent LLM workflows using agent orchestration frameworks such as LangGraph, LangGraph4j, or LangChain4j, including declarative graph compilation, conditional routing between agent nodes, state persistence, and dynamic agent spawning at runtime.
  • Build and maintain ReAct agent loops in which an LLM selects tools, interprets tool results, and continues until a terminal answer is produced.
  • Implement supervisor/orchestrator agent patterns in which a lead agent plans, spawns, and coordinates specialist sub-agents using conditional graph edges and tool frameworks.
  • Develop MCP tool servers that expose enterprise data sources as callable tools for AI agents to provide natural-language access to structured backend systems.
  • Implement and refine RAG pipelines for document ingestion and chunking (PDF/Word), embedding generation, vector storage (such as PostgreSQL with pgvector), and semantic similarity retrieval that grounds LLM responses in authoritative source documents.
  • Integrate with enterprise LLM inference gateways through REST APIs, including request shaping, prompt template management, token budget control, and graceful degradation during service unavailability.
  • Design and implement agent memory architectures, including isolated short-term per-agent context stores and shared long-term knowledge repositories.
  • Build confidence-scoring and uncertainty-quantification mechanisms for AI-generated outputs, routing low-confidence results to human review based on thresholds.
  • Implement agent feedback loops that capture human reviewer decisions and feed structured corrections into prompt templates and knowledge repositories to improve model behavior.
  • Write LangChain4j/LangChain tool definitions, such as Java @Tool-annotated methods, and connect them to ReAct agents for data transformation, external API calls, and domain-specific processing.
  • Build and maintain agentic audit trails that log tool calls, agent decisions, LLM prompts and responses, and state transitions for traceability and explainability.
  • Develop and test Spring Boot microservices using Java 21 and Spring Boot 3.5.x to host AI orchestration engines, expose REST APIs to downstream consumers, and integrate with upstream data source systems.
  • Author and maintain Helm charts and Jenkins CI/CD pipelines for deploying containerized AI services to Kubernetes/OpenShift clusters across multi-region on-premise data centers.
  • Build and maintain 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.
Desired Qualifications
  • At least 3 years of work experience with a Bachelor’s degree, or more than 2 years of work experience with an advanced degree.
  • At least 2 years of relevant work experience and a Bachelor’s degree, or at least 5 years of relevant work experience.
  • At least 3 years of work experience with a Bachelor’s degree or more than 2 years of work experience with an advanced degree.
  • Experience developing backend services in Java 17 or later; Java 21 is a strong plus.

About the company

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, California

Founded

1958

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Simplify's Take

What believers are saying

  • Visa demand keeps growing: July 2026 results showed quarterly revenue up 14% to $11.6 billion.
  • Advisory staff get structured training: Visa University has over 50 learning modules for its consulting and analytics teams.
  • Visa is capturing stablecoin activity: September 2026 settlement volume exceeded a $20 billion annualized run rate.

What critics are saying

  • Visa must pay partners more: July 2026 results showed client incentives up 18%, faster than revenue.
  • Visa faces pressure on its debit contracts: the Justice Department seeks restrictions on alleged anticompetitive practices.
  • Technology and product jobs face cuts: Visa announced about 2,600 planned layoffs in July 2026.

What makes Visa unique

  • Visa reaches everyday checkout at scale: its cards are accepted at over 175 million merchant locations.
  • Visa avoids consumer lending exposure: it bears no cardholder credit risk.
  • Visa Direct extends beyond cards: it reaches approximately 12 billion bank account, wallet and card endpoints.

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Benefits

  • Health Insurance.
  • Life Insurance.
  • Dental Insurance.
  • Disability Insurance.
  • Accidental Death & Dismemberment Insurance.

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Business Wire
May 15th, 2025
Nekuda Raises $5M Led by Madrona, Together with Amex Ventures and Visa Ventures, to Power Agentic Payments

Nekuda raises $5M led by Madrona, Amex Ventures & Visa Ventures to power agentic aayments. For the infrastructure of the future of online commerce.

Forbes
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Visa Invests in BVNK, $50M Funding

Visa Ventures has invested in stablecoin infrastructure platform BVNK, marking Visa's first direct investment in a stablecoin payments player. This move supports the future of stablecoins in payments, enhancing BVNK's credibility and penetration with Visa's partners. BVNK, which raised $50 million in a Series B round in December 2024, processes $12 billion in annualized volume and offers solutions for B2B and B2C payments using stablecoins.