{"schemaVersion":"jobsearcher.job.v1","id":"73b2cd728ef3d9f2ab5055ef","url":"https://jobsearcher.com/jobs/73b2cd728ef3d9f2ab5055ef","canonicalUrl":"https://jobsearcher.com/jobs/73b2cd728ef3d9f2ab5055ef","title":"Machine Learning Engineer - Fraud Detection","description":"Role: Machine Learning Engineer - Fraud DetectionLocation: Dallas, TX (100% Onsite)Experience: 7-12 YearsRole SummaryWe are looking for a Machine Learning Engineer to build and support production-grade fraud detection solutions. The role focuses on real-time inference, feature engineering, APIs, graph-based fraud detection, and production deployment support.Key SkillsMachine Learning Engineering and Real-Time InferencePython, APIs, and MicroservicesGoogle Cloud Platform and DatabricksNeo4j / Graph Databases and Feature StoresData Pipelines and Feature EngineeringMLOps, Monitoring, and Production SupportAgentic AI Architecture (good to have)ResponsibilitiesBuild and deploy fraud detection services for production use.Develop low-latency inference solutions with a target of less than 250 ms.Design feature engineering pipelines for ML use cases.Integrate ML models with REST APIs and microservices.Support graph-based fraud detection using Neo4j.Improve scoring performance, reliability, and scalability.Work with MLOps teams for releases, monitoring, and production support.Support data quality, governance, and operational activities.Required QualificationsHands-on experience in Python and ML model deployment.Experience with APIs, microservices, and production ML systems.Knowledge of data pipelines, data engineering, and feature stores.Exposure to Google Cloud Platform, Databricks, Data Lake, or Data Warehouse platforms.Basic understanding of MLOps, monitoring, and release support.Good communication and problem-solving skills.Nice to HaveFraud detection, risk analytics, or scoring model experience.Experience with Neo4j or graph-based ML solutions.Understanding of Agentic AI architecture.","company":"Compugra Systems","rawCompany":"compugra systems","city":"Dallas","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-08-15T10:26:24.918Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer - Fraud Detection","description":"Role: Machine Learning Engineer - Fraud DetectionLocation: Dallas, TX (100% Onsite)Experience: 7-12 YearsRole SummaryWe are looking for a Machine Learning Engineer to build and support production-grade fraud detection solutions. The role focuses on real-time inference, feature engineering, APIs, graph-based fraud detection, and production deployment support.Key SkillsMachine Learning Engineering and Real-Time InferencePython, APIs, and MicroservicesGoogle Cloud Platform and DatabricksNeo4j / Graph Databases and Feature StoresData Pipelines and Feature EngineeringMLOps, Monitoring, and Production SupportAgentic AI Architecture (good to have)ResponsibilitiesBuild and deploy fraud detection services for production use.Develop low-latency inference solutions with a target of less than 250 ms.Design feature engineering pipelines for ML use cases.Integrate ML models with REST APIs and microservices.Support graph-based fraud detection using Neo4j.Improve scoring performance, reliability, and scalability.Work with MLOps teams for releases, monitoring, and production support.Support data quality, governance, and operational activities.Required QualificationsHands-on experience in Python and ML model deployment.Experience with APIs, microservices, and production ML systems.Knowledge of data pipelines, data engineering, and feature stores.Exposure to Google Cloud Platform, Databricks, Data Lake, or Data Warehouse platforms.Basic understanding of MLOps, monitoring, and release support.Good communication and problem-solving skills.Nice to HaveFraud detection, risk analytics, or scoring model experience.Experience with Neo4j or graph-based ML solutions.Understanding of Agentic AI architecture.","datePosted":"2026-08-15T10:26:24.918Z","dateModified":"2026-08-15T10:26:24.918Z","hiringOrganization":{"@type":"Organization","name":"Compugra Systems","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dallas","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"73b2cd728ef3d9f2ab5055ef"},"url":"https://jobsearcher.com/jobs/73b2cd728ef3d9f2ab5055ef"}}