Hybrid Real-Time Fraud ML Engineer

Q2 India; North Carolina; Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Hybrid work options

Flexible time off

Career development & mentoring

Volunteer programs

Job summary

Q2 is a leading provider of digital banking and lending solutions. We seek a Machine Learning Engineer to build and operate production systems behind fraud detection at scale, safeguarding trillions of transactions annually.

You will collaborate with data scientists and engineers to turn models into reliable, real-time platforms and continuously improve performance. You will work on model development, evaluation, deployment, monitoring, and measurement, with a focus on low latency, reliability,

Qualifications

• Bachelor’s degree in a related field and at least 2 years of relevant ML experience.
• Proven experience in ML model development and deployment.
• Strong knowledge of statistics, optimization, probability, and experimental methodologies.
• Proficiency in Python, R, or Java.
• Experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Familiarity with cloud platforms and scalable computing resources.
• Strong analytical, problem-solving, and collaboration skills.

Responsibilities

• Research emerging fraud patterns and translate that research into new detection approaches.
• Help build next-generation ML products across identity, behavior, and transaction fraud, partnering directly with customers to understand their needs and shape product direction.
• Build and optimize real-time, low-latency ML infrastructure, continually improving its reliability, scalability, and performance.
• Build and maintain systems and pipelines that support training, evaluation, and inference for machine learning models, collaborating with data scientists to productionalize models into scalable applications.
• Write clean, maintainable, and well-tested code, following production engineering best practices and leveraging the latest AI tooling.
• Support monitoring and troubleshooting of production ML systems, including data pipelines and model performance.

Skills

ML model development

ML deployment

Statistics

Optimization

Experiment design

Python

R

Java

Cloud platforms

Analytical thinking

Collaboration

Education

Bachelor's degree in related field

Tools

TensorFlow

PyTorch

scikit-learn

Job description

Q2 is a leading provider of digital banking and lending solutions. We seek a Machine Learning Engineer to build and operate production systems behind fraud detection at scale, safeguarding trillions of transactions annually.

You will collaborate with data scientists and engineers to turn models into reliable, real-time platforms and continuously improve performance. You will work on model development, evaluation, deployment, monitoring, and measurement, with a focus on low latency, reliability,