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Software Engineer Fraud Detection Jobs in Texas (NOW HIRING)

Q2 Software, Inc.

Senior Fraud Response Engineer

Austin, TX · On-site

$103K - $142K/yr

SUMMARY As a Senior Fraud Response Engineer , you will play a critical role in supporting the Fraud ... Design, develop, and maintain fraud detection, monitoring, and response capabilities across cloud ...

Q2 India

Senior Fraud Response Data Analyst

Austin, TX · On-site

$120 - $160/hr

You will partner closely with Product, Security, Engineering, and Customer Support teams to develop meaningful metrics, evaluate fraud detection strategies, and provide executive-level reporting.The ...

Q2

Senior Fraud Response Engineer

Austin, TX

$103K - $142K/yr

SUMMARY As a Senior Fraud Response Engineer , you will play a critical role in supporting the Fraud ... Design, develop, and maintain fraud detection, monitoring, and response capabilities across cloud ...

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Software Engineer Fraud Detection information

What does a software engineer fraud detection do?

A Software Engineer in Fraud Detection designs and develops systems to identify and prevent fraudulent activities within digital platforms, such as banking or e-commerce environments. They build algorithms to analyze user behavior, detect anomalies, and flag suspicious transactions in real time. Their work often involves machine learning, big data analysis, and close collaboration with data scientists and security teams to continuously improve fraud detection accuracy. These engineers play a key role in protecting businesses and customers from financial loss and cybercrime.

How does a software engineer fraud detection typically collaborate with data scientists and analysts to identify fraudulent activity?

Software Engineers in Fraud Detection work closely with data scientists and analysts to build, refine, and deploy systems that detect and prevent fraud. While data scientists may develop models and identify patterns from large datasets, engineers are responsible for integrating these models into scalable, real-time systems within the company's technology stack. Regular communication and joint problem-solving are essential, as engineers must understand the logic behind models and analysts' findings to ensure accurate implementation and continuous improvement. This collaborative environment helps create robust fraud detection mechanisms that adapt to evolving threats.

What are the key skills and qualifications needed to thrive as a software engineer fraud detection, and why are they important?

To thrive as a Software Engineer in Fraud Detection, strong programming skills (such as Python, Java, or Scala), a solid understanding of algorithms, data structures, and experience with machine learning or statistical analysis are generally required, often supported by a degree in computer science or a related field. Familiarity with big data platforms (like Hadoop or Spark), real-time analytics systems, and fraud detection tools or frameworks is typically expected. Analytical thinking, problem-solving abilities, and effective communication are key soft skills that differentiate top performers in this field. These skills are crucial for developing robust systems that can quickly identify and prevent fraudulent activities, protecting both users and organizations.

What is the difference between Software Engineer Fraud Detection vs Data Scientist Fraud Detection?

AspectSoftware Engineer Fraud DetectionData Scientist Fraud Detection
Required CredentialsBachelor's in CS or related field, programming skillsBachelor's or higher in CS, Statistics, or Data Science
Work EnvironmentDevelops fraud detection systems, writes code, implements algorithmsAnalyzes data, builds models, interprets results
Employer & Industry UsageFinancial institutions, fintech, e-commerceFinancial services, tech companies, insurance
Common Search & ComparisonFocuses on software development for fraud detectionFocuses on data analysis and modeling for fraud detection

While both roles work in fraud detection, Software Engineer Fraud Detection primarily develops and maintains detection systems through coding, whereas Data Scientist Fraud Detection analyzes data and builds models to identify fraudulent activity. Both roles often collaborate but differ in their core focus and skill sets.

What are popular job titles related to Software Engineer Fraud Detection jobs in Texas?

For Software Engineer Fraud Detection jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Software Engineer Fraud Detection jobs in Texas look for?

The top searched job categories for Software Engineer Fraud Detection jobs in Texas are:

What cities in Texas are hiring for Software Engineer Fraud Detection jobs?

Cities in Texas with the most Software Engineer Fraud Detection job openings:

Software Engineer Fraud Detection jobs near you

Lead MLOps Engineer - Fraud Detection Platform

Dallas, TX • On-site

Compugra Systems
11 - 50 employees
Learn more about Compugra Systemsexternal

$101K - $134K/yr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Role: Lead MLOps Engineer - Fraud Detection Platform

Location: Dallas, TX (100% Onsite)

6-12 months

Experience: 8-12 Years

Job Summary: We are seeking a Lead MLOps Engineer to build and manage enterprise-scale fraud detection platforms. The candidate will be responsible for designing scalable ML deployment frameworks, automating model release processes, and ensuring production reliability, monitoring, and governance across fraud detection systems.

Required Skills

Primary Skills

  • MLOps
  • CI/CD
  • Model Deployment
  • Google Cloud Platform
  • Monitoring & Observability
  • Agentic AI Architecture

Secondary Skills

  • Kubernetes
  • Infrastructure Automation
  • Security Controls
  • Site Reliability Engineering (SRE)
  • DevOps

Key Responsibilities

  • Design and implement enterprise MLOps platforms and deployment standards.
  • Build model registry and automated release pipelines.
  • Automate model deployment and rollback mechanisms.
  • Establish end-to-end monitoring, alerting, and observability.
  • Manage feature store and inference infrastructure.
  • Ensure platform reliability, scalability, and performance.
  • Implement governance, compliance, and audit controls.
  • Drive production readiness and operational excellence across ML services.

Responsibilities Area

  • ML Solution Design & Architecture
  • Model Deployment & Production Operations
  • Performance Optimization
  • Data Engineering & Feature Engineering
  • Monitoring & Continuous Improvement
  • Platform Governance & Security

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