Sr. Software Engineer - Core Automation Platform

Visa•Ashburn, VA

3d•$110,700 - $171,800•Hybrid

About The Position

Visa's Technology organization is a global community of innovators solving some of the most complex challenges in commerce. We operate one of the world's largest and most sophisticated payment networks, processing more than 65,000 secure transactions per second across 80M+ merchants, 15,000 financial institutions, and billions of consumers worldwide. Joining Visa means working on highly distributed, large-scale systems that power the future of payments, data solutions, cybersecurity, AI, and next-generation customer experiences. The Opportunity: Global Operations is at the heart of Visa's production ecosystem, ensuring our critical services operate with exceptional reliability, availability, and performance for clients around the world. As we accelerate our adoption of Automation, Generative AI, and AIOps, we're transforming how operational excellence is delivered at scale. This team is responsible for building intelligent, resilient, and future-ready operational capabilities across Visa's technology landscape. In this role, you will: Help shape the future of AI-driven operations for one of the world's largest payment platforms. Build and scale intelligent automation, observability, and operational solutions for highly integrated, high-throughput systems. Leverage GenAI and AIOps to improve incident detection, response, and system reliability. Collaborate with world-class engineers to solve complex technical challenges at global scale. Contribute to a culture that values innovation, continuous learning, blameless postmortems, and proactive problem solving.

Requirements

2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience
Generative AI frameworks and tools such as LangGraph, LangChain, Haystack, AWS Bedrock, or similar platforms.
Backend development using Python, with FastAPI strongly preferred.
Frontend development using React and TypeScript.
Event-driven architectures and messaging technologies such as Kafka/MQ/SQS
Vector databases and retrieval technologies such as FAISS, Pinecone, Weaviate, OpenSearch, or similar solutions.
CI/CD automation and developer platforms, including GitHub, Bitbucket, Jira, Jenkins, ServiceNow, and related tooling.
Experience in one or more of the following areas is highly desirable: Distributed systems, microservices architecture, and API design.
Retrieval-Augmented Generation (RAG) implementations at scale using cloud-based or enterprise vector stores.
Building and deploying solutions on AWS, including Lambda, ECS/EKS, S3, API Gateway, Bedrock, and SageMaker.
Event-driven architectures and messaging technologies such as Kafka or Kinesis.
Developing reusable platforms, frameworks, or developer tools that accelerate adoption across engineering teams.
Payments, financial services, risk management, fraud analytics, or other highly regulated industries.

Nice To Haves

2-5 years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)
Bachelor’s or Master’s in Computer Science, Engineering, AI/ML, or related field.
Experience working within Agile development environments, including SAFe and globally distributed teams

Responsibilities

Design and build reusable GenAI platforms, SDKs, pipelines, and shared frameworks that accelerate AI adoption across engineering teams.
Develop solutions using agent frameworks such as LangGraph, LangChain, Haystack, or similar technologies.
Implement modern AI architectures leveraging AWS services including Bedrock, SageMaker, embeddings, vector databases, and agentic workflows.
Design and optimize Retrieval-Augmented Generation (RAG) architectures, LLM orchestration layers, evaluation frameworks, and LLMOps processes.
Design and develop production-ready AI applications using Python, FastAPI, Flask, Node.js, Kafka for backend, and React, TypeScript for frontend.
Build secure APIs, microservices, event-driven architectures, and enterprise integrations.
Create intuitive, scalable user experiences for AI-powered products and platforms.
Implement CI/CD automation using tools such as GitHub Actions, Jenkins, Bitbucket, Jira, ServiceNow, and Artifactory.
Apply containerization, automated testing, infrastructure-as-code (Terraform, CloudFormation), and deployment best practices.
Ensure robust monitoring, logging, observability, and recovery strategies are built into every solution.
Partner closely with cybersecurity teams to deliver secure, enterprise-grade AI solutions in production.