AI Integration Analyst – Risk Analytics

Texas Capital•Dallas, TX

About The Position

Texas Capital Bank is seeking an enthusiastic and detail-oriented AI Integration Specialist to join the Risk Analytics group. In this role, you will work alongside experienced risk professionals to help design, build, and deploy intelligent AI agents that support critical risk and compliance functions. This is an excellent opportunity to progress your career in AI development while contributing to meaningful, high-impact projects in a regulated banking environment.

Requirements

Bachelor's degree in Computer Science, Data Science, Software Engineering, Mathematics, or a related field, or equivalent hands-on experience.
5+ years of professional work experience
2+ years building AI-powered applications, intelligent agents, or LLM-based workflows.
2+ years of experience in banking, financial services, or risk/compliance domain with hands-on exposure to financial risk concepts
Familiarity with SQL, data manipulation, analytics, or basic data engineering concepts.
Foundational knowledge of AI and LLMs, including basic understanding of prompt engineering, API usage, and how language models work.
Strong Python proficiency with demonstrated ability to write clean, readable, and well-tested code.
Familiarity with credit risk concepts such as risk ratings, portfolio segmentation, credit review processes, loan underwriting, or compliance/audit frameworks.
Basic understanding of data governance, and responsible AI principles.
Previous work in regulated industries or exposure to audit and governance frameworks
Understanding of core AI agent concepts such as tool use, memory management, multi-step workflows, and agent orchestration patterns.
Ability to translate business requirements into technical designs with guidance from senior team members.
Strong communication and collaboration skills with demonstrated ability to learn quickly and work effectively with analytics and business teams.
Detail-oriented approach with genuine commitment to code quality, testing, data accuracy, and production reliability.
Eagerness to learn with a growth mindset and enthusiasm for developing expertise in AI development and risk analytics.

Nice To Haves

Preferred internship or coursework experience in machine learning, NLP, or AI development.
Preferred experience as a developer, technical lead, or architect in financial services or risk-focused environments.
Experience with data visualization tools, business intelligence platforms, or reporting systems.
Experience with APIs, cloud platforms (Azure, AWS, or GCP), or similar enterprise tools.
Exposure to financial services, banking, or credit risk concepts.
Knowledge of version control (Git)

Responsibilities

Design and develop AI agents using our enterprise agent-building platform under guidance from senior developers, with a focus on supporting Risk Analytics and Credit Risk functions.
Collaborate with Risk Analytics and credit teams to understand business requirements, translate them into agent designs, and validate outputs against analytics expectations.
Build and test agent logic including prompt engineering, API integration, tool use, and multi-step reasoning flows to support risk reporting and analytics workflows.
Integrate agents with internal data systems, analytics platforms, and APIs with support from senior engineers, learning enterprise integration best practices.
Support Risk Analytics with dashboard development, data workflows, and regulatory reporting, learning to translate complex risk data into actionable insights for stakeholders.
Own the complete AI agent lifecycle including building, testing, maintenance, and monitoring in production with guidance, developing skills in troubleshooting, optimization, and continuous improvement.
Write clear, maintainable code and comprehensive documentation for agent designs, enabling knowledge transfer across the Risk Organization and supporting ongoing analytics operations.
Monitor agent performance and data quality in production, learning to identify anomalies, validate outputs against business expectations, and drive improvements to analytics accuracy.
Stay current with advancements in large language models (LLMs), agentic frameworks, and enterprise AI tooling as they apply to risk analytics and financial data processing.
Ensure all agent outputs meet data quality, auditability, and compliance standards required in our regulated banking environment.
Participate in team meetings and communities of practice to share learnings and contribute to the growing AI development practice within the bank.