Enterprise Technology - Applied AI Engineer

Job Overview:
The Applied AI Engineer works within the Data and AI team to identify practical AI opportunities, develop prototypes, and build production‑ready solutions that enable NBH Bank to adopt AI responsibly across its operations. The role is a foundational individual‑contributor position that bridges business, technology, and risk domains.

Identify and assess AI opportunities by working directly with business, operations, risk, technology, and data partners. Separate high‑value ideas from low‑value ones and translate the best opportunities into safe, usable, and measurable solutions. Facilitate discovery sessions, map workflows, evaluate where AI, automation, search, summarization, agents, or decision‑support tools can responsibly improve outcomes, and prioritize based on business value, feasibility, risk, and operational readiness.

Design, evaluate, and deliver AI‑powered solutions across the organization. Build or partner with application teams to create AI‑enabled tools, retrieval‑augmented generation solutions, workflow automations, agent‑based patterns, data integrations, and evaluation frameworks that validate AI capabilities before and after deployment.

Act as a practical AI advisor and coach for associates, running workshops, office hours, and hands‑on sessions for both technical and business teams. Produce playbooks, reusable patterns, prompt guidance, and documentation, staying current on AI developments and translating new capabilities into actionable recommendations for NBH’s technology stack and operating model.

Collaborate with Information Security, AI governance, Risk, application engineering, and business leaders to ensure AI solutions are developed responsibly and align with banking, model risk, compliance, privacy, and security expectations.

• Bachelor's degree in computer science, Information Systems, or a related field.
• 3+ years of experience in software engineering, data, automation, business technology, process improvement, AI implementation, or similar applicable experience.
• 2+ years of hands‑on experience applying LLMs, AI tools, automation, or AI‑enabled solutions to real business or technical problems, with demonstrated judgment of where AI creates value, introduces risk, or should not be used.
• Proficiency in Python or another modern programming language used for automation, data, or application development.
• Experience reading and writing SQL against enterprise data warehouses such as Snowflake, SQL, and PostgreSQL.
• Demonstrated regular use of modern AI tools, with clear judgment about where they create value, introduce risk, or should not be used.
• Experience evaluating AI, machine learning, automation, or decision‑support solutions for accuracy, risk, usability, and business value.
• Familiarity with APIs, version control systems such as Git Hub or Bitbucket, and common software delivery practices.
• Experience working with cross‑functional business, technology, data, risk, and/or operations teams in a fast‑paced environment.
• Strong verbal, written, and interpersonal communication skills, including the ability to facilitate discovery, teach, influence, and present effectively to both technical and business audiences.
• Driving may be required; applicants must have a valid driver’s license and a driving history that meets the Bank’s driver standards.

• 5+ years of relevant job experience.
• Advanced degree, certification, or equivalent advanced training related to software engineering, data, AI, machine learning, analytics, business process improvement, or digital transformation.
• Experience in financial services, banking, fintech, or another regulated industry.
• Working familiarity with model risk management expectations such as SR 11-7, OCC, and FFIEC as applied to AI systems.
• Experience identifying, sizing, and prioritizing AI or automation opportunities in business operations, lending, banking, contact center, compliance, risk, finance, or data workflows.
• Experience designing pilots, proofs of concept, adoption programs, or enablement practices that move AI ideas from exploration to measurable operational value.
• Experience with workflow…