1 day ago
Bengaluru, IndiaEntry Level / Mid Level
Responsibilities
- Build and maintain scalable data pipelines, APIs, microservices, and distributed applications for fraud intelligence and risk solutions.
- Develop batch and real-time data-processing applications.
- Build and integrate Generative AI capabilities, including intelligent search, summarization, copilots, RAG, and agent-based workflows.
- Design reliable and resilient services using retries, timeouts, circuit breakers, idempotency, failure isolation, and graceful degradation.
- Implement monitoring, structured logging, distributed tracing, dashboards, alerts, and service-health indicators.
- Develop automated unit, integration, performance, resiliency, and deployment-validation tests.
- Identify production risks, bottlenecks, recurring failures, and performance issues and contribute corrective actions.
- Participate in production support, incident triage, root-cause analysis, and post-incident improvements.
- Maintain operational documentation, troubleshooting guides, alerts, and runbooks.
- Collaborate with Product, Data Science, Platform Engineering, Site Reliability Engineering, and Security teams.
- Use AI-assisted development tools to improve productivity, software quality, testing, and delivery consistency.
Requirements
- Bachelor’s or master’s degree in Computer Science, Engineering, Data Science, or a related technical field.
- 6 months to 2 years of software engineering experience developing enterprise applications, data platforms, distributed systems, or cloud-native solutions.
- Strong programming skills in Java and/or Python.
- Experience building APIs, microservices, data pipelines, or distributed applications.
- Understanding of data structures, algorithms, object-oriented design, and software engineering fundamentals.
- Experience with automated testing, source control, code reviews, and CI/CD practices.
- Foundational understanding of application monitoring, structured logging, alerting, and production troubleshooting.
- Strong analytical, problem-solving, communication, and collaboration skills.
- Preferred experience with Kafka, Spark, relational databases, data modeling, and batch or real-time data pipelines.
- Preferred familiarity with LLM applications, RAG, vector databases, prompt engineering, and agentic AI frameworks.
- Preferred experience with cloud platforms, Docker, Kubernetes, CI/CD, and DevOps practices.
- Preferred exposure to performance testing, capacity planning, incident management, and root-cause analysis.
- Experience in fraud, cybersecurity, financial services, or large-scale data platforms is a plus.
Benefits
- Visa requires at least 3 days in the office, with specific expectations confirmed by the Hiring Manager.
- Visa is an EEO Employer.
Categories
AI ApplicationsData Engineering
About Visa
Visa builds and operates a global digital payments network that connects consumers, merchants, financial institutions, and governments. It earns fees for processing card transactions and offering risk, data, and value‑added services through VisaNet and products like tokenization and tap‑to‑pay. Founded in 1958 and headquartered in Foster City, California, Visa is a public company (NYSE: V) serving clients in more than 200 countries and territories.