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Generative AI Engineer

This role is for a Generative AI Engineer with a contract length of "unknown" and a pay rate of "unknown." It requires 2+ years in Generative AI and 5+ years in ML/DL, expertise in compliance-driven solutions, and proficiency in Python, TensorFlow, and cloud services.
🌎 - Country
United States
💱 - Currency
$ USD
💰 - Day rate
Unknown
Unknown
🗓️ - Date discovered
February 22, 2025
🕒 - Project duration
Unknown
🏝️ - Location type
Unknown
📄 - Contract type
Unknown
🔒 - Security clearance
Unknown
📍 - Location detailed
Concord, CA
🧠 - Skills detailed
#Data Science #Compliance #Monitoring #AWS (Amazon Web Services) #Forecasting #Azure #Langchain #Mathematics #Model Deployment #Documentation #Automation #Computer Science #Hugging Face #Python #GCP (Google Cloud Platform) #Deep Learning #ML (Machine Learning) #Cloud #Security #Model Validation #Deployment #AI (Artificial Intelligence) #BERT #PyTorch #TensorFlow #NLP (Natural Language Processing)
Role description
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Job Summary

We are seeking a Generative AI Engineer to join our team and drive AI innovation in the Liquidity Management & Regulatory Compliance space. The ideal candidate will have at least 2 years of hands-on experience in Generative AI and 4+ years of experience in Machine Learning (ML) and Deep Learning (DL). This role will focus on compliance, contract lifecycle management, and regulatory alignment, ensuring that AI-driven solutions meet the highest standards of governance and safety within a highly regulated financial environment.

Key Responsibilities

  1. AI Development & Implementation
    • Design, develop, and deploy Generative AI models (LLMs, NLP, and other deep learning frameworks) to automate and enhance liquidity management, risk assessment, and compliance workflows.
    • Build AI-driven solutions for contract lifecycle management, optimizing document processing, policy interpretation, and regulatory reporting.
    • Fine-tune and adapt Large Language Models (LLMs) to align with financial regulations, bank policies, and risk management frameworks.

  2. Compliance & Regulatory Alignment
    • Ensure AI models adhere to financial industry regulations (Basel III, Dodd-Frank, SR 11-7, OCC guidelines, etc.) and internal risk frameworks.
    • Work closely with Legal, Compliance, and Risk teams to integrate AI solutions while maintaining model transparency and explainability.
    • Support AI model validation, bias detection, and risk mitigation strategies to align with Wells Fargo’s AI governance policies.

  3. AI Model Risk & Governance
    • Develop rigorous documentation and model audit trails to comply with regulatory requirements.
    • Monitor AI model drift, fairness, interpretability, and security, ensuring ethical AI usage in financial decision-making.
    • Implement AI safety and bias mitigation techniques to align with responsible AI principles.

  4. Collaboration & Stakeholder Engagement
    • Partner with Liquidity Risk, Treasury, and Compliance teams to identify AI-driven efficiencies in liquidity forecasting and regulatory reporting.
    • Work with IT, Data Science, and Risk Management teams to deploy AI solutions in production while ensuring robust monitoring and security.
    • Contribute to AI research and innovation within Wells Fargo, staying ahead of emerging AI regulations and industry best practices.

Required Qualifications
• Master’s or PhD in Computer Science, Machine Learning, AI, Applied Mathematics, or a related field.
• 2+ years of hands-on experience with Generative AI, LLMs, and NLP-based models (e.g., GPT, BERT, Llama, T5).
• 5+ years of experience in Machine Learning (ML), Deep Learning (DL), and AI model deployment.
• Expertise in compliance-driven AI solutions and working within highly regulated environments (preferably in banking/finance).
• Proficiency in Python, TensorFlow, PyTorch, LangChain, Hugging Face, and cloud AI services (AWS, Azure, GCP).
• Strong knowledge of AI safety, explainability, and ethical AI principles.
• Experience with AI model governance, validation, and bias mitigation techniques.

Preferred Qualifications
• Prior experience working in liquidity management, risk modeling, or regulatory compliance.
• Knowledge of financial regulations related to AI applications in banking (e.g., SR 11-7, OCC AI guidelines).
• Hands-on experience with contract lifecycle automation using AI-driven solutions.
• Familiarity with MLOps frameworks and model monitoring best practices.