AI Integration Sr. Analyst - Risk Analytics

Overview of the Position Texas Capital Bank is seeking an experienced AI Integration Senior Analyst to join the Risk and Compliance group. This role is focused on designing, building, and deploying sophisticated AI agents using the bank's enterprise agent-building platform, with a strong emphasis on risk management and compliance workflows. The ideal candidate brings substantial banking and risk/compliance experience combined with advanced AI development expertise, enabling them to serve as a technical leader and trusted advisor to Risk Analytics and the broader Risk Organization. Responsibilities Lead technical strategy and agent-based workflows, ensuring alignment with enterprise standards and regulatory requirements. Design and develop production-grade AI agents to support credit risk, portfolio monitoring, compliance reviews, and other mission-critical risk functions. Collaborate with Risk leadership and domain experts to translate complex credit, compliance, and risk workflows into scalable, auditable AI-powered solutions. Integrate agents with internal data systems, APIs, and enterprise tools to enable end-to-end automation while maintaining data governance and compliance standards. Build, test, and iterate on advanced agent logic including prompt engineering, tool use, memory management, and multi-step reasoning flows. Mentor junior analysts and contribute to internal best practices, documentation, and the AI community of practice. Own the entire AI agent’s lifecycle, providing maintenance, troubleshooting issues, and driving continuous improvements with a focus on accuracy and regulatory compliance. Stay current with advancements in large language models (LLMs), agentic frameworks, and enterprise AI tooling. Ensure all agent outputs meet stringent data quality, auditability, and compliance standards required in a highly regulated banking environment. Support Risk Analytics with dashboards and other data driven solutions aimed at extracting actionable insights. Contribute to broader AI/ML strategy and help establish governance frameworks for responsible AI deployment. Qualifications Bachelor’s Degree in Computer Science, Data Science, Data Engineering, Software Engineering, Mathematics, or a related field, or equivalent hands-on experience. Master’s Degree preferred 7+ years of professional experience 3+ years specifically building AI-powered applications, intelligent agents, or LLM-based workflows. 3+ years of experience in banking, financial services, or risk/compliance domain with hands-on exposure to financial risk concepts. Advanced proficiency in Python and demonstrated expertise building multi-step AI workflows with LLMs — including prompt engineering, tool integration, memory management, agent orchestration patterns, and multi-agent systems. Proven experience integrating AI systems with APIs, databases, enterprise data platforms, and legacy banking systems. Deep familiarity with credit risk concepts such as risk ratings, portfolio segmentation, credit review processes, loan underwriting, or compliance/audit frameworks. Experience as a technical lead in financial services or risk-focused environments. Deep understanding of prompt engineering, retrieval-augmented generation (RAG), agent tool use patterns, and reasoning frameworks. Ability to understand, translate, and architect complex business, risk, and compliance workflows into scalable technical agent designs. Strong analytical and problem-solving skills with the ability to balance innovation with regulatory compliance and risk management. Experience building evaluation frameworks and benchmark suites to systematically assess agent accuracy, reliability, and robustness, including testing across edge cases, adversarial inputs, and real-world use case variability. Excellent communication and leadership skills — able to influence technical and non-technical stakeholders, mentor analysts, and drive alignment across teams. Demonstrated commitment to detail, data accuracy, and quality assurance, especially in regulated or high-stakes environments. Demonstrated experience designing and implementing Model Context Protocol (MCP) servers to enable secure, structured communication between AI agents and external tools, APIs, and enterprise resources. Proven ability to build and maintain scalable ETL pipelines that ingest, transform, and deliver data in agent-ready formats, including quality validation, error handling, and automated monitoring. Solid full-stack engineering skills spanning backend APIs, frontend interfaces, and relational/non-relational databases, with a strong emphasis on clean code practices, automated testing, and CI/CD pipelines. Hands-on experience with enterprise AI platforms in production environments. Advanced knowledge of data engineering, SQL, data pipelines, data governance, and cloud platforms (Azure, Snowflake, AWS or similar). Prior experience mentoring teams or leading cross-functional AI or data-driven initiatives. #J-18808-Ljbffr