Lead ML Engineer

Role: Lead ML Engineer - Fraud Detection

Location: Austin, TX (100% Onsite) - No flexibility

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
• Google Cloud Platform 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 Google Cloud Platform, 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.