Corporate Treasury, Dallas, Vice President, Software Engineering

Key Responsibilities

• Design, develop, and deploy machine learning and AI models to support liquidity risk metrics, stress scenarios, early-warning indicators, and forecasting.
• Build end-to-end AI pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring.
• Apply supervised, unsupervised, and time-series modeling techniques to large-scale financial and transactional datasets.
• Partner with liquidity risk managers and quantitative teams to translate regulatory and business requirements into AI-driven solutions.
• Optimize agents' performance, scalability, and reliability in distributed and cloud-based environments.
• Contribute to the firm's AI engineering standards, including testing, model documentation, and production controls.
• Mentor junior engineers and contribute to code reviews, design discussions, and architecture decisions.

Required Qualifications

• 5+ years of professional experience as an AI Engineer in a production environment.
• Hands-on experience in integrating LLM models using agents and developing monitoring and observability tools for those agents.
• Experience with AWS Bed Rock platform especially using AWS Agent core for deploying agents.
• Experience in developing agents using Google ADK or Lang Graph frameworks and deploying them on AWS.
• Exposure to distributed computing frameworks and workflow orchestration tools (e.g., Airflow).
• Strong proficiency in Python and experience with ML/AI libraries such as PyTorch, or similar.
• Solid understanding of machine learning fundamentals, including model selection, bias-variance trade-offs, and evaluation techniques.
• Experience working with large, structured datasets using SQL and distributed data platforms (cloud data warehouses).