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# Lead Artificial Intelligence Engineer

- **Company:** Credit One Bank
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Computer Programming, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Natural Language Processing, Tensorflow, Azure Machine Learning, Search Technologies, Transaction Data, Reinforcement Learning, Feature Engineering, Pytorch, Large Language Models, Snowflake, Prompt Engineering, Deep Learning, Generative AI, Containerization, Kubernetes, Information Technology, Data Management, Machine Learning Operations, Azure Synapse Analytics, Data Pipelines, Databricks, Microservices
- **Published:** June 9, 2026
- **Apply:** https://www.dice.com/job-detail/e4a8727a-fca4-453c-a0c0-44419269e2b6

## About the Role

* 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.

## Description

The Lead Artificial Intelligence Engineer develops and produces AI and machine learning solutions supporting banking and credit card businesses. This role focuses on building scalable, explainable, and compliant AI models for fraud detection, credit risk, customer analytics, and operational intelligence., * 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.

Position Requirements

Core AI Concepts and Technologies Required:

 * Machine Learning & Modeling
 + Supervised, unsupervised, reinforcement learning
 + Deep learning (CNNs, RNNs, Transformers)
 + Natural Language Processing (NLP) & LLMs
 + Generative AI (diffusion models, fine-tuning, RAG)

 * AI Engineering & MLOps
 + Model training, deployment, monitoring, and retraining
 + Feature stores, vector databases, and model registries
 + CI/CD pipelines for ML (MLOps)
 + GPU/accelerator compute architectures

 * Cloud & Infrastructure
 + Azure AI, Azure ML, AWS Sagemaker, or Google Vertex AI
 + Kubernetes, containerization, microservices
 + Data platforms (Databricks, Snowflake, Synapse)

 * Responsible AI & Governance
 + Model explainability (SHAP, LIME)
 + Fairness, bias detection, model risk controls
 + Privacy-preserving ML techniques (differential privacy, federated learning)

 * Programming & Tooling
 + Python, PyTorch, TensorFlow, JAX
 + LangChain, semantic search, vector embeddings
 + Prompt engineering & LLM orchestration frameworks


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