Machine Learning Engineer Fraud Detection
Overview
You will own production-grade fraud detection models for a Dallas-based fintech, scaling to 8-12 years of expertise. Join a team of 5 engineers and data scientists, partnering with risk and platform teams. This contract role stands out for its focus on real-time inference and graph analytics, requiring deep GCP and Databricks proficiency.
What You'll Do8
- 1Build real-time fraud scoring models using Python and GCP services, reducing false positives by 30%.
- 2Design low-latency inference APIs with REST and Microservices, handling 10K requests per second.
- 3Develop feature engineering pipelines in Databricks, processing streaming data for fraud signals.
- 4Integrate Neo4j graph databases for entity link analysis and fraud ring detection.
- 5Implement MLOps practices with Kubeflow and MLflow for model deployment and monitoring.
- 6Optimize feature stores to serve real-time features with sub-100ms latency.
- 7Collaborate with data engineering to build reliable Kafka pipelines for event ingestion.
- 8Drive model performance evaluation and drift detection to maintain accuracy.
Requirements8
- 18-12 years in machine learning and software engineering.
- 25+ years building production fraud detection systems with Python and Scikit-learn.
- 33+ years with real-time inference using TensorFlow or PyTorch.
- 4Expertise in Google Cloud Platform (Vertex AI, BigQuery) and Databricks.
- 5Strong experience with Neo4j and graph algorithms for fraud analytics.
- 6Proficient in REST API development with FastAPI or Flask.
- 7Hands-on with Kafka for streaming data pipelines.
- 8Deep understanding of feature stores and feature engineering.
Salary Insight
Salary not disclosed in listing
Location
Required Skills
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