Staff Software Engineer – Agentic AI Platform

Visa•Bellevue, WA

23h•Hybrid

About The Position

Visa is building a next-generation Agentic AI Platform that brings intelligent, autonomous agents into large-scale distributed applications across our global ecosystem. We’re seeking a Staff Software Engineer who will design and build scalable backend systems that integrate AI agents into Visa’s enterprise infrastructure. You will work with other engineers and technical leaders to define the architecture, scaling strategy, and engineering standards for Visa’s AI-driven products. This is a role for a self-driven engineer — a go-getter, builder, and problem solver with deep experience in Java microservices, GenAI integration, and distributed system design, and a true-north, entrepreneurial mindset focused on speed, quality, and innovation.

Requirements

5+ years of relevant work experience with a Bachelor’s 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, OR 8+ years of relevant work experience.

Nice To Haves

6+ years of backend software development experience with Java and Spring Boot.
2+ years of backend experience with Python (FastAPI, Flask, or similar frameworks).
Production-level experience in building and deploying AI Agentic solutions, including orchestration of LLM-based agents, integration with enterprise microservices, and troubleshooting live production environments.
Hands-on experience implementing LLM, GenAI, and RAG systems using frameworks such as LangChain, LangGraph, or Autogen.
Strong understanding of AI orchestration and LLM integration patterns in distributed environments.
Proven experience building and scaling distributed microservice architectures.
Proficiency in REST/gRPC API design, event-driven architectures, and message queues (Kafka, RabbitMQ).
Experience with vector databases (Pinecone, Weaviate, FAISS) and retrieval-augmented generation (RAG) pipelines.
Hands-on expertise with cloud platforms (AWS, GCP, Azure).
Familiarity with SQL and NoSQL databases (MySQL, DynamoDB, MongoDB).
Experience with containerization and CI/CD pipelines (Docker, Kubernetes, ArgoCD, Jenkins, GitHub Actions).
Excellent debugging, performance optimization, and system design skills at scale.
Go-Getter: Executes decisively, drives clarity, and thrives in ambiguity.
Builder: Hands-on technologist who delivers high-impact systems from prototype to production.
Hustler: Operates with urgency and persistence to overcome obstacles.
Entrepreneurial: Innovates beyond defined scope, with ownership and bias for action.
True North: Leads with integrity, clarity, and long-term vision.
Lead by Example: Demonstrates accountability, openness, and excellence in technical and people leadership.
Execute with Excellence: Sets a high bar for quality, collaboration, and delivery across multi-disciplinary engineering teams.

Responsibilities

Architect and evolve the Agentic AI Platform to support multi-agent orchestration, retrieval-augmented generation (RAG), and integration with existing Visa microservices.
Design scalable, secure backend systems using Java (Spring Boot) and Python (FastAPI/Flask).
Develop RESTful and gRPC APIs that connect backend services, AI agents, and React front-end applications.
Integrate Large Language Models (LLMs) such as GPT, Claude, Mistral, and Gemini into backend systems.
Build and optimize RAG pipelines using vector databases (Pinecone, Weaviate, FAISS).
Develop orchestration and agentic workflows using LangChain, LangGraph, or Autogen.
Lead the design and implementation of distributed, fault-tolerant systems that scale horizontally.
Implement auto-scaling, monitoring, and observability using Kubernetes, Docker, and Prometheus/Grafana.
Drive best practices for latency reduction, cost optimization, and service reliability.
Partner with other senior engineers, ML engineers, and product leads to define and deliver the platform roadmap.
Mentor developers, set coding standards, and lead design reviews.
Deliver results with urgency — balancing innovation with enterprise rigor.
Explore new frameworks, architectures, and deployment models to push the boundaries of Agentic AI in production.
Drive continuous improvement, automation, and open-source adoption across the engineering organization.