Visa is building a next-generation services that brings intelligent, autonomous agents into large-scale distributed applications across our global ecosystem. We’re seeking a Sr. Consultant Software Engineer who will architect, design, and build scalable backend systems. 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 role for a hands-on technical leader — 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.
Architecture & System Design:
- Design multi-tier, distributed systems using Java (Spring Boot) with clear domain boundaries and API contracts.
- Use REST for efficient service-to-service communication between microservices, AI agents, and front-end applications.
- Architect and evolve backend microservice platforms that support GenAI-driven capabilities, agent orchestration, and MCP-based model interactions.
- Design and implement Model Context Protocol (MCP) servers and clients to standardize model access, tool invocation, and context exchange across AI services.
- Ensure security-by-design, including authentication, authorization, data privacy, and compliance with enterprise standards.
Backend Microservices Engineering (Core Focus):
- Develop and own high-performance backend microservices using Java, Spring Boot, Kafka, MySQL and Event-driven and asynchronous processing patterns
- Lead the design of highly scalable, low-latency services that meet strict availability and throughput requirements.
- Apply engineering standards, SDLC best practices, and design patterns across the lifecycle of large-scale systems.
- Design and optimize database schemas, queries, and data access layers, ensuring reliability and performance at scale
- Independently deliver and evolve large, mission-critical applications and complex multi-tier solutions.
Generative AI & Developer Productivity:
- Apply context engineering techniques, including use of memory banks, conversation state, and external knowledge sources, to improve relevance and consistency of AI‑powered features.
- Design and maintain prompt engineering strategies to ensure reliable, secure, and high‑quality LLM responses across backend services.
- Implement and integrate Model Context Protocol (MCP) patterns to enable structured tool access, context sharing, and interoperability between AI agents and backend APIs.
- Integrate AI agents into production applications, enabling task automation, decision support, or intelligent workflows within backend services.
- Leverage AI‑assisted development tools (e.g., Claude, GitHub Copilot, or similar) in day‑to‑day coding activities to improve development velocity, code quality, and maintainability.
- Establish best practices for human‑in‑the‑loop workflows, fallback handling, and observability for AI‑driven features.
Scalability, Reliability & Observability:
- Design fault-tolerant, horizontally scalable systems using Kubernetes and Docker
- Use Prometheus, Grafana, and distributed tracing tools for auto-scaling, monitoring, and alerting.
- Drive latency reduction, optimize costs, and enhance resiliency across backend and AI-enabled services.
- Use telemetry and production metrics to propose and implement architectural and performance enhancements.
Quality Engineering & DevOps:
- Design and build test automation frameworks (unit, integration, contract, load) to deliver quality software.
- Conduct code and design reviews to ensure all requirements are met.
- Support deployment pipelines, staging environments, and production releases.
- Develop and maintain deployment, rollback, and operations runbooks for large-scale systems.
Global Collaboration & Cross-Time zone Leadership:
- Collaborate closely with engineering teams in the United States and India, operating effectively across time zones.
- Maintain the ability to work overlap hours to support real-time design discussions, incident response, and delivery milestones.
- Lead solution design and technical alignment with senior engineers and leads across geographies.
- Synthesize inputs from multiple teams to propose the best technical solutions, balancing scalability, maintainability, and business impact.
- Act as a knowledge multiplier, sharing best practices, architectural insights, and emerging trends across regions.
Technical Leadership & Collaboration:
- Partner with product managers, system engineers, and engineering teams to define and execute the technical roadmap.
- Mentor senior and mid-level engineers, setting coding standards, architectural patterns, and best practices
- Provide technical guidance across teams, leveraging backend and AI platform expertise to unblock complex initiatives
- Balance innovation velocity with enterprise discipline to ensure solutions are production-ready and sustainable over the long term.
Innovation, Strategy & Continuous Improvement:
- Evaluate and introduce new frameworks, architectures, and deployment models for backend systems and GenAI platforms.
- Drive adoption of open-source technologies, automation, and platform reusability.
- Shape long-term strategy for microservice based backend services, including governance, cost controls, and developer experience.
- Champion continuous improvement across code quality, system design, and operational excellence.
Key Differentiators of This Role
- Backend-first Staff Engineer II with deep ownership of microservices and distributed systems.
- Production-grade GenAI integration, not experimental AI work.
- MCP-driven architecture to standardize model access, tooling, and agent interactions.
- Strong emphasis on scalability, reliability, and enterprise standards
This is a hybrid position. Expectation of days in the office will be confirmed by your Hiring Manager.