Machine Learning Engineer - Fraud Detection

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.

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.