Lead AI Engineer: Banking Fraud & Risk Analytics

Federal Home Loan Bank of Dallas; Las Vegas (NV); On-site; USD 140,000 - 210,000; Full time

Job summary

Credit One Bank, N.A. seeks a Lead Artificial Intelligence Engineer to design and deploy scalable, explainable ML/AI solutions for fraud detection, credit risk, and analytics. The role emphasizes MLOps, governance, and regulatory documentation while collaborating with risk and compliance teams.

Responsibilities include model development, training, deployment, monitoring, and retraining across cloud platforms and data platforms for large-scale financial data.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
• 3–7 years of experience in AI/ML or data science.
• Experience working with large-scale financial or transactional data is preferred.

Responsibilities

• Develop, train, and optimize ML, deep learning, and Generative AI models.
• Implement data pipelines, feature engineering, and model inference services.
• Deploy and monitor models using enterprise MLOps practices.
• Support model explainability, bias analysis, and regulatory documentation.
• Collaborate with data engineers, risk, and compliance teams.

Skills

Supervised learning

Unsupervised learning

Reinforcement learning

Deep learning

NLP

LLMs

Generative AI

MLOps

Feature stores

Vector databases

Model registries

CI/CD for ML

GPU compute

Azure ML

SageMaker

Databricks

Snowflake

Synapse

Python

PyTorch

TensorFlow

JAX

LangChain

Vector embeddings

Education

Bachelor’s degree in Computer Science, Engineering, Data Science, or related field

Tools

Databricks

Snowflake

Synapse

Kubernetes

Docker

Git

MLflow

Job description

Credit One Bank, N.A. seeks a Lead Artificial Intelligence Engineer to design and deploy scalable, explainable ML/AI solutions for fraud detection, credit risk, and analytics. The role emphasizes MLOps, governance, and regulatory documentation while collaborating with risk and compliance teams.

Responsibilities include model development, training, deployment, monitoring, and retraining across cloud platforms and data platforms for large-scale financial data.