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# Machine Learning Engineer - Fraud Detection

- **Company:** Peoplentech Llc
- **Location:** Alexandria, VA, United States
- **Experience:** Expert
- **Salary:** $145,600
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Information Engineering, Data Warehousing, Fraud Prevention and Detection, Graph Database, Python (Programming Language), Machine Learning, Neo4j, Software Deployment, Feature Store, Scripting, Google Cloud, Feature Engineering, Agentic-AI, Build Management, Data Lakes, Real-time Inference, Data Management, Machine Learning Operations, Restful APIs, Data Pipelines, Databricks, Microservices
- **Published:** November 7, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-engineer-fraud-detection-alexandria-va--a782452a-f03d-4399-8182-7601aa51c480

## About the Role

Machine Learning, Machine Learning, Application Programming Interface (API), Production Support, Python Programming/Scripting Language, Microservices, GCP (Good Clinical Practices), Neo4j, Data Management, Production Control, * 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), * 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.

## Description

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., * 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.