Software Engineer

Visa is hiring for the role of Software Engineer!

Responsibilities of the Candidate:

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.

Requirements:

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 how you used them, what they got wrong, and how you verified the result

Familiarity with SSDLC, Git and a normal code review workflow

Experience in developing and maintaining data pipelines and large-scale data processing jobs.

Experience building an agentic or LLM-powered application — tool calling, retrieval, multi-step orchestration, or an MCP-style integration

Experience evaluating non-deterministic systems: writing evals, building regression suites for prompts or agents, or measuring agent task success

Regular use of an agentic coding environment such as Claude Code, and a considered opinion on where it accelerates work and where it should not be trusted

Experience taking a model from a notebook to something that runs on a schedule or serves live traffic

Exposure to model serving infrastructure — Triton Inference Server, TorchServe, ONNX Runtime, or similar

Exposure to containers and Kubernetes

Experience with a workflow orchestrator such as Airflow, Dagster, or Kubeflow

Coursework, competition placements, publications, or open-source contributions in ML

Experience in optimizing code for performance, cost efficiency, and maintainability.

Experience in collaborating with cross-functional teams to translate business requirements into technical solutions.