Software Engineer - Sr. Consultant Level-4

Visa Visa · Fintech · Bengaluru, India, IN

Software Engineer role at Visa focused on integrating GenAI/LLM solutions into B2B payment platforms. Responsibilities include designing, developing, and maintaining AI features, working with vector databases, embeddings, prompt engineering, and RAG architectures. The role requires experience in building production-grade AI features and optimizing GenAI systems for enterprise use cases within a fintech domain.

What you'd actually do

  1. Work as a member of a team responsible for architecting, designing, coding, testing and maintaining Visa's Business Solutions B2B line of products.
  2. Lead architecture and design for the applications you own. Contribute expertise for other application by participating in review sessions.
  3. Lead collaboration with stakeholders and uses understanding of tradeoffs and project costs to determine requirements for a project
  4. Translate functional and non-functional requirements into system designs and communicates how the components will interact
  5. Develop code that complies with design specifications and meets security and Java/J2EE best practices. Use industry standard design patterns where applicable.

Skills

Required

  • Java
  • Spring
  • Hibernate
  • Java and web frameworks at scale
  • GIT/Stash
  • Maven
  • Jenkins
  • NoSQL datastores
  • SQL datastores
  • MySQL
  • MongoDB
  • Unix or Linux platforms
  • Docker/Kubernetes
  • Kafka
  • HazelCast
  • Prometheus
  • Graphana
  • tools development
  • automation (CI/CD, Auto Deployment, System Availability, etc.)
  • latest web technologies (TCP/IP, HTTP, HTML, JavaScript, CSS, Angular / React / Jquery, NodeJs)
  • REST
  • SOAP
  • XML
  • JSON
  • OAuth
  • SAML

Nice to have

  • Eclipse/MyEclipse
  • JetBrains IntelliJ
  • GenAI/LLM-based solutions (e.g., OpenAI, Azure OpenAI, Anthropic, Vertex AI)
  • vector databases
  • embeddings
  • prompt engineering
  • RAG architectures
  • model optimization techniques
  • scalable, high-performance GenAI systems
  • building architecture of an application ground up including infra sizing, tech stack identification and performance estimation

What the JD emphasized

  • Hands-on experience designing, fine-tuning, and integrating GenAI/LLM-based solutions (e.g., OpenAI, Azure OpenAI, Anthropic, Vertex AI), with a track record of building production-grade AI features or developer tooling.
  • Deep understanding of vector databases, embeddings, prompt engineering, RAG architectures, and model optimization techniques, with the ability to architect scalable, high-performance GenAI systems for enterprise use cases.

Other signals

  • GenAI/LLM-based solutions
  • production-grade AI features
  • vector databases
  • embeddings
  • prompt engineering
  • RAG architectures
  • model optimization techniques
  • scalable, high-performance GenAI systems