Enterprise Technology - Applied AI Engineer

NBH Bank

1d•Hybrid

About The Position

As an Applied AI Engineer, you will join the Data and AI team in a foundational individual contributor role as NBH Bank builds its applied AI practice across NBH and its affiliated brands. As an Applied AI Engineer, you will help identify practical AI opportunities, build safe prototypes and production-ready solutions, and help NBH adopt AI responsibly. You will work directly with business, operations, risk, technology, and data partners to understand how work gets done today, separate high-value AI opportunities from low-value ideas, and turn the best opportunities into safe, usable, measurable solutions. As an Applied AI Engineer, you will have an opportunity to design, evaluate, and deliver AI-powered solutions that address meaningful business problems across the organization. You will facilitate discovery sessions with stakeholders, map workflows and pain points, assess where AI, automation, search, summarization, agents, or decision-support tools can responsibly improve outcomes, and help prioritize opportunities based on business value, feasibility, risk, and operational readiness. When solutions require engineering, you will build or partner with application teams to build 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. In this role, you will also serve as a practical AI advisor and coach for associates across the Bank. You will run workshops, office hours, and hands-on working sessions for both technical and business teams; create playbooks, reusable patterns, prompt guidance, and documentation; stay current on relevant AI developments; and translate new capabilities into practical recommendations for NBH’s technology stack and operating model. You will partner with Information Security, AI governance, Risk, application engineering, and business leaders to help ensure AI solutions are developed responsibly and align with applicable banking, model risk, compliance, privacy, and security expectations.

Requirements

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 about where AI creates value, where it introduces risk, and where it 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, where they introduce risk, and where they 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 GitHub 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.
Applicants must have a valid driver's license (may not be learner’s, restricted or “to and from work” license) and driving history that meets the Bank’s driver standards. This information will be verified in a Motor Vehicle Report.

Nice To Haves

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 automation, process mining, RPA, knowledge management, enterprise search, or digital transformation initiatives.
Experience with Azure, Snowflake, dbt, APIs, and modern application or data platforms.
Prior consulting, business analysis, product, forward-deployed engineering, staff-engineering, developer relations, or enablement experience where influencing and coaching others was an explicit part of the role.
Strong analytical and problem-solving skills, with good judgment about what not to build, automate, or deploy with AI.
Ability to prioritize competing demands, manage multiple tasks or projects, research and resolve problems independently, and make sound and timely decisions.
Passion for practical AI adoption, responsible innovation, measurable efficiency improvement, and helping fellow associates build confidence with new tools and practices.

Responsibilities

Identify practical AI opportunities.
Build safe prototypes and production-ready AI solutions.
Help NBH adopt AI responsibly.
Work directly with business, operations, risk, technology, and data partners.
Understand how work gets done today.
Separate high-value AI opportunities from low-value ideas.
Turn the best opportunities into safe, usable, measurable solutions.
Design, evaluate, and deliver AI-powered solutions that address meaningful business problems.
Facilitate discovery sessions with stakeholders.
Map workflows and pain points.
Assess where AI, automation, search, summarization, agents, or decision-support tools can responsibly improve outcomes.
Prioritize opportunities based on business value, feasibility, risk, and operational readiness.
Build or partner with application teams to build AI-enabled tools, retrieval-augmented generation solutions, workflow automations, agent-based patterns, data integrations, and evaluation frameworks.
Serve as a practical AI advisor and coach for associates across the Bank.
Run workshops, office hours, and hands-on working sessions for both technical and business teams.
Create playbooks, reusable patterns, prompt guidance, and documentation.
Stay current on relevant AI developments.
Translate new capabilities into practical recommendations for NBH’s technology stack and operating model.
Partner with Information Security, AI governance, Risk, application engineering, and business leaders to ensure AI solutions are developed responsibly and align with applicable banking, model risk, compliance, privacy, and security expectations.