Senior Software Engineer - Java / Python / AWS

Job Description

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

Key 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.

AI GenAI Integration:

• 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.

Scalability Reliability:

• 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.

Leadership Execution:

• 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.

Innovation Strategy:

• 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.

AI Security Governance:

• Design secure, compliant Agentic AI systems with built-in safeguards for data privacy, prompt security, auditability, and responsible AI usage across enterprise environments.

LLM Quality Evaluation:

• Define evaluation metrics and testing frameworks to measure LLM and agent performance, accuracy, latency, cost, and reliability in production.

Platform Developer Enablement:

• Build reusable AI platform components, SDKs, and internal tooling that accelerate adoption and enable teams to safely develop, deploy, and scale Agentic AI solutions.

Observability Explainability:

• Implement end-to-end observability for AI workflows, including tracing, logging, and monitoring of agent decisions and tool usage across distributed systems.

Operational Excellence:

• Lead production readiness, incident response, and postmortems for AI services, driving continuous improvements in resilience, scalability, and deployment practices.

CrossFunctional Technical Leadership:

• Collaborate with product, data, ML, and security teams to translate business needs into scalable AI architectures and influence platform strategy and roadmap.

This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

Qualifications

Basic Qualifications: Bachelors or Masters degree in Computer Science, Engineering, or related field. 10+ 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. Experience building and integrating Model Context Protocol (MCP) frameworks for tool and API interoperability. Strong understanding of AI orchestration and LLM integration patterns in distributed environments. Proven…