Software Engineer (1-2 years' experience, Python / Go, ML Engineering)

About Us
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.

Job Description

Software and ML Engineers conduct, plan, or oversee one or more aspects of the analysis, design, programming, debugging, and modification of computer programs for commercial or end user applications. They write code, complete programming, and perform testing and debugging of applications. Responsibilities also include completing documentation and procedures for installation and maintenance. Software Engineers may interface with users to define system requirements and/or necessary modifications or develop cost estimates, budgets, and schedules.

All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools (e.g. ChatGPT, Microsoft Copilot) to support everyday work.

Key Responsibilities:

• Write, modify, and review high-quality, testable, and efficient code across front-end, back-end, and data layers.
• Write, modify, and review high-quality, testable, and efficient code across data, model training, and inference layers.
• Efficient use of AI tooling to build, integrate, and support agentic flows within engineering and business workflows.
• Develop and maintain feature engineering pipelines and large-scale data processing jobs using appropriate frameworks and cloud services.
• Train, tune, and evaluate machine learning models, maintaining reproducible experiment tracking and clear documentation of results.
• Package, deploy, and serve models in production using containerization, orchestration, and model serving frameworks.
• Integrate models and services via APIs, messaging queues, feature stores, and databases.
• Ensure parity between training-time and serving-time feature computation, and resolve discrepancies when identified.
• Develop unit, integration, and end-to-end tests with a strong focus on automation, including tests for data quality and model behavior.
• Build and maintain evaluation suites for non-deterministic components such as LLM and agentic workflows, with measurable success criteria.
• Maintain CI/CD pipelines to run tests, validate models, and deploy to environments automatically upon code commits.
• Debug and perform root cause analysis on model and pipeline issues, using AI-driven tools where applicable, and enforce code quality gates through reviews and static analysis.
• Monitor models and applications in production using observability tools;
  investigate incidents, analyze drift, identify root causes, and implement fixes.
• Optimize models and code fst efficiency, and maintainability.
• Apply secure coding practily, validate inputs, andensure data handling and access controls are robust.
• Comply with regulatory ands and maintain documentation for audits and model risk review.

Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager. 

Qualifications

Basic Qualifications:

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field.
• 1-2 years of professional Software Engineering or ML Engineering experience
• Experience in writing, modifying, and reviewing code for commercial or end user applications.
• Experience in debugging and performing root cause analysis on system and ML issues.
• Experience in developing unit, integration, and end-to-end tests.
• Experience in integrating systems via APIs, messaging queues, and databases.
• Experience in applying secure coding practices and complying with regulatory standards.
• Working knowledge of core ML concepts: supervised learning, train/validation/test discipline, overfitting, class imbalance, and the difference between accuracy and a metric that actually matters
• Practical experience with at least one ML framework — scikit-learn, XGBoost, PyTorch, or TensorFlow
• Demonstrated hands-on use of LLM-based or agentic tools in real work, and the ability to explain concretely …