Corporate Treasury, Dallas, Vice President, Software Engineering

We are seeking an AI Engineer with 5+ years of experience to join the Liquidity Risk technology team. In this role, you will design, build, and deploy AI‑driven solutions that enhance liquidity risk monitoring, stress testing, scenario generation, and decision support. You will work closely with liquidity risk managers, quantitative teams, and engineering partners to translate complex risk problems into scalable, production‑ready AI systems.

• 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.

• 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).

• Opportunity to work at the intersection of AI, engineering, and liquidity risk at a global scale.
• High‑impact role influencing how the firm measures and manages liquidity under stress.
• Collaborative environment with exposure to senior risk managers, quants, and technology leaders.
• Ongoing learning, development, and career progression within the Liquidity and Engineering organizations.