Full Stack Software Architect; Sr. Consultant), Generative AI

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.

We are looking for a seasoned Software Architect (Sr. Consultant title at Visa) to join our Corporate Generative AI Technologies team. In this role, you will help architect, build, and scale enterprise-grade Generative AI and agentic applications. This is a senior, hands-on engineering role for someone who brings strong system design judgment, full-stack product engineering depth, and the ability to translate complex business workflows into scalable, reliable AI-enabled automation solutions.

You will work on platforms and applications that use AI-native application patterns, including large language models, agentic workflows, retrieval-augmented generation, tool orchestration, API integrations, ETL pipelines, and data systems to transform business processes into intelligent, reliable, and scalable workflows. You will help solve hard engineering problems such as decomposing complex workflows into reusable agents, designing secure human-in-the-loop systems, and building production-grade GenAI applications that can be monitored, evaluated, governed, and continuously improved.

As a Senior Consultant (Staff Software Architect), you will operate with a high degree of autonomy, ownership, and technical judgment while collaborating closely with your manager and the broader engineering team to align on technical direction. You will be responsible for building high-quality software while also influencing system design decisions, architectural direction, engineering standards, and best practices across the team.

• Design, build, and scale enterprise-grade GenAI and agentic applications, with a strong focus on maintainable, secure, scalable, reliable, and production-ready architecture.
• Own architecture and delivery of major GenAI subsystems; lead design reviews; mentor I4/I5 engineers; define reusable patterns and production standards.
• Apply strong system design judgment to build full-stack, production-grade applications with robust API design, workflow orchestration, secure data flows, observability, and operational readiness.
• Build modern frontend experiences using React and established frontend patterns, including component-based architecture, state management, reusable UI components, accessibility, performance optimization, and seamless integration with backend APIs and AI-enabled services.
• Design and implement scalable backend services using Python, Node.js, and/or Java, including secure APIs, asynchronous processing, background jobs, authentication, authorization, logging, error handling, and system resiliency.
• Work with databases like PostgreSQL, Redis, vector databases, and related technologies, including schema design, indexing strategies, query optimization, transaction management, caching patterns, migrations, and data access patterns.
• Implement backend capabilities for AI-enabled and agentic workflow automation, including intent routing, agent orchestration, tool execution, API integrations, data retrieval, multi-step execution, workflow state management, human approval flows, guardrails, auditability, and enterprise system integration.
• Develop AI-native capabilities using OpenAI, Anthropic, and related LLM APIs/SDKs, including prompt orchestration, tool/function calling, structured outputs, streaming responses, model routing, and evaluation patterns.
• Apply deep knowledge of modern LLM capabilities to make informed engineering decisions around model selection, context management, latency, cost, reliability, output quality, safety, and user experience.
• Design and implement retrieval-augmented generation solutions, including ingestion pipelines, ETL workflows, embeddings, vector database integration, retrieval strategies, relevance ranking, grounding, and retrieval quality evaluation.
• Build and deploy cloud-native applications using containers, Dev Ops practices, CI/CD pipelines, automated testing, monitoring, and operational automation.
• Work closely with engineering teammates and cross-functional partners to align with team priorities, translate ambiguous requirements into proof-of-concepts, then evolve them into production-quality solutions through shared ownership and hands-on collaboration.
• Implement observability and operational excellence for GenAI applications, including end-to-end tracing, workflow telemetry, model evaluation, and monitoring to ensure secure, reliable, and…