Senior AI & Fraud Analytics Engineer

ComTec Information Systems (IT)

Richmond (VA)

On-site

USD 160,000 - 230,000

Full time

4 days ago
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Job summary

ComTec Information Systems (IT) seeks a Senior/Staff/Principal-level AI & Fraud Analytics Engineer to build and scale production AI/ML platforms for fraud detection, AML, transaction monitoring, and financial crime analytics.

You will implement real-time scoring, anomaly detection, and graph ML, and apply LLM/RAG/GenAI to fraud use cases, while ensuring security, privacy, and auditability across data pipelines and models.

Qualifications

  • 14–17 years of Software/AI/ML/Data/Platform Engineering experience.
  • Proficient in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn/XGBoost).
  • Experience with MLOps tools and production ML pipelines.
  • Experience with fraud domains: AML, KYC, sanctions, transaction monitoring.
  • Knowledge of real-time scoring, anomaly detection, graph ML.
  • Familiarity with Databricks, Snowflake, Delta Lake.
  • Understanding of security, privacy, compliance & auditability.

Responsibilities

  • Design and scale production AI/ML platforms for fraud detection and financial crime analytics.
  • Implement real-time fraud scoring and anomaly detection.
  • Develop ML models for AML, KYC, sanctions and transaction monitoring.
  • Apply LLM/RAG/GenAI to fraud use cases.
  • Ensure security, privacy, and auditability of data and models.

Skills

Python
ML frameworks
MLOps
Real-time scoring
Fraud analytics
Graph ML
GenAI
Security/compliance

Tools

MLflow
Kubeflow
SageMaker
Ray
Kafka
Spark
Databricks
Snowflake
Delta Lake

Job description

ComTec Information Systems (IT) seeks a Senior/Staff/Principal-level AI & Fraud Analytics Engineer to build and scale production AI/ML platforms for fraud detection, AML, transaction monitoring, and financial crime analytics.

You will implement real-time scoring, anomaly detection, and graph ML, and apply LLM/RAG/GenAI to fraud use cases, while ensuring security, privacy, and auditability across data pipelines and models.

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