The role focuses on real-time inference, feature engineering, APIs, graph-based fraud detection, and production deployment support. We are looking for a Machine Learning Engineer to build and support production-grade fraud detection solutions.
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.