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

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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

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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

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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

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Nice to Hav

• eFraud detection, risk analytics, or scoring model experience
• .Experience with Neo4j or graph-based ML solutions
• .Understanding of Agentic AI architecture