Real-Time Fraud ML Engineer

Q2 India; Cary (NC); Hybrid; USD 110,000 - 160,000; Full time

Benefits offered by this job

Hybrid work

Flexible PTO

Mentoring programs

Health insurance

Parental leave

Volunteer programs

Peer recognition

Job summary

Q2 is hiring a Machine Learning Engineer to strengthen its Risk & Fraud team. You will build production ML systems that detect fraud at scale, impacting trillions of transactions for banks, credit unions, and fintechs.

You will collaborate with data scientists and engineers to turn models into reliable, real-time services, continuously improving performance, latency, and monitoring in production. This applied role emphasizes end-to-end ML pipelines, from data preprocessing to deployment.

Qualifications

• Bachelor’s degree in a related field with 2+ years of ML experience.
• Proven experience in ML model development and deployment.
• Strong knowledge of statistics, optimization, probability theory, and experimental methodologies.
• Proficiency in programming languages such as Python, R, or Java.
• Experience with ML frameworks/libraries (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.
• 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

Python

R

Java

TensorFlow

PyTorch

scikit-learn

Cloud platforms

Education

Bachelor’s degree

Job description

Q2 is hiring a Machine Learning Engineer to strengthen its Risk & Fraud team. You will build production ML systems that detect fraud at scale, impacting trillions of transactions for banks, credit unions, and fintechs.

You will collaborate with data scientists and engineers to turn models into reliable, real-time services, continuously improving performance, latency, and monitoring in production. This applied role emphasizes end-to-end ML pipelines, from data preprocessing to deployment.