Core Engineering, Dallas, Vice President, Software Engineering Dallas Vice President

The Core Engineering, Dallas, Vice President, Software Engineering  Dallas, TX, United States The Core Engineering

The Core Engineering builds and operates the platforms, applications, data solutions, models, and analytics that power critical processes for The Core divisions of the firm (e.g., Risk, responsible for the risk profile of firm activities;
Controllers, responsible for the financial control and reporting obligations;
Compliance, responsible for the firm’s compliance, regulatory, and reputational risks;
Corporate Treasury, responsible for the firm’s liquidity, funding, balance sheet, etc.; and Human Capital Management, responsible for attracting, developing, and managing a global workforce). A centralized engineering structure in support of The Core enables a common platform model and operating framework that promotes consistent governance and scalable solutions, leveraging cloud, AI, and machine learning for innovation and efficiency. The Core Engineering’s 2,000+ engineers and strats deliver engineering, data, analytics, and quantitative capabilities within six business units:

Metrics & Analytics Platforms: responsible for the measurement and management of the firm’s risk, capital, and liquidity for The Core functions

The Core Strats: responsible for the development and implementation of models and other quantitative methodologies, including the accuracy and attribution of modeled metrics

Financials & Reporting: responsible for facilitating the production of the firm’s financials and a wide range of reporting functions

Non-Financial Risk & Controls: responsible for non‑financial risk and control processes

Enterprise Platforms: responsible for platforms and applications that support critical operational processes across The Core such as payments, people processes, and procurement

Shared Services: responsible for driving the adoption of consistent engineering strategy, including data platforms, cloud, and AI enablement, as well as the management of technology risk

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 Py Torch , 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…