Machine Learning Engineer - Fraud Detection

Role: Machine Learning Engineer - Fraud Detection

Location: Dallas, TX (100% Onsite)

Experience: 7-12 Years

Role Summary

We 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 Skills

• Machine Learning Engineering and Real-Time Inference

• Python, APIs, and Microservices

• GCP and Databricks

• Neo4j / Graph Databases and Feature Stores

• Data Pipelines and Feature Engineering

• MLOps, Monitoring, and Production Support

• Agentic AI Architecture (good to have)

Responsibilities

• Build 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 Qualifications

• Hands-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 Have

• Fraud detection, risk analytics, or scoring model experience.

• Experience with Neo4j or graph-based ML solutions.

• Understanding of Agentic AI architecture.