Machine Learning Engineer

Role: Machine Learning Engineer - Fraud Detection Location: Dallas, TX (100% Onsite) Role Summar yWe 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 Skill • sMachine Learning Engineering and Real-Time Inferenc • ePython, APIs, and Microservice • sGCP and Databrick • sNeo4j / Graph Databases and Feature Store • sData Pipelines and Feature Engineerin • gMLOps, Monitoring, and Production Suppor • tAgentic AI Architecture (good to have ) Responsibilitie • sBuild 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 Qualification • sHands-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 GCP, Databricks, Data Lake, or Data Warehouse platforms • .Basic understanding of MLOps, monitoring, and release support • .Good communication and problem-solving skills . Nice to Hav • eFraud detection, risk analytics, or scoring model experience • .Experience with Neo4j or graph-based ML solutions • .Understanding of Agentic AI architecture .