Software Engineer - Senior Consultant

Company Description

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.

“This role qualifies for Autorskie Koszty Uzyskania Przychodu (KUP), in accordance with applicable Polish tax regulations. Eligible employees may benefit from preferential tax treatment on income derived from the creation of intellectual property, subject to meeting statutory criteria.”

Job Description

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