Software Development Engineer- Product Reliability Engineering

Visa Visa · Fintech · Austin, TX

Software Development Engineer on the Product Reliability Engineering (PRE) team at Visa. This role focuses on building and automating large-scale systems for payment transactions, with a significant emphasis on developing agentic AI tools to enhance deployment pipelines, infrastructure provisioning, and observability. The role involves managing data platforms, optimizing database performance, and ensuring system reliability and security within a regulated financial technology environment.

What you'd actually do

  1. Build GenAI-powered engineering assistants that automate deployment orchestration, release governance, and environment lifecycle management.
  2. Integrate LLMs into observability, incident response, and developer support workflows, transforming reactive operations into proactive, AI-driven intelligence.
  3. Contribute to prompt engineering, model fine-tuning, and agentic automation initiatives that position PRE as one of the most AI-forward reliability organizations in financial technology.
  4. Design and ship end-to-end automation for deployment pipelines, infrastructure provisioning, and release orchestration — code that runs millions of times so engineers never have to repeat themselves.
  5. Write clean, production-grade Python (and Go or Bash where it counts) to eliminate toil, reduce manual intervention, and make systems self-managing.

Skills

Required

  • Python
  • SQL
  • data modeling
  • query optimization
  • database connectivity
  • systems design
  • algorithms
  • data structures

Nice to have

  • Go
  • Bash
  • Ansible
  • Liquibase
  • Prometheus
  • Grafana
  • Splunk
  • ELK
  • relational database systems
  • real-time event streaming architectures

What the JD emphasized

  • agentic AI tools
  • GenAI-powered engineering assistants
  • Integrate LLMs
  • prompt engineering, model fine-tuning, and agentic automation

Other signals

  • Build GenAI-powered engineering assistants
  • Integrate LLMs into observability, incident response, and developer support workflows
  • prompt engineering, model fine-tuning, and agentic automation initiatives