Prompt Engineer

Diné Development Corporation•Dayton, OH

About The Position

The Prompt Engineer works with an agile team to provide high-quality service to customers managing a portfolio of systems. The role explores and develops AI proofs of concept related to data management and enterprise architecture, and supports analysis and research into team- and enterprise-wide processes. It documents AI use case journeys so they can be clearly communicated to different audiences and their limitations understood. The position applies analytical techniques to identify solutions based on client requirements within an IT services and solutions scope. It analyzes and reverse-engineers business and technical artifacts—including data models, data catalogs, design documentation, and interfaces—to capture, store, and maintain relevant metadata for systems in the portfolio.

Requirements

At least 3 years of experience.
Bachelor of Science degree.
Must be able to obtain a Secret clearance.
Experience with Python.
Experience architecting AI solutions.
Experience in context engineering.
Experience with multiple LLM vendors and their models.
Experience in one or more scripting languages.
Excellent written and verbal communication skills.
Experience with Agile development methodologies and roles.

Nice To Haves

Experience using SQL for data analysis, data quality, and data profiling.
Experience creating data mapping documentation for databases, interfaces, and applications.
Experience with AF Logistics systems and data.
Experience in requirements elicitation.
Experience with StackAI.
Experience developing RAG solutions.
Experience with agentic development frameworks, such as Claude Code.
Experience in information management, including data design, data transformation, data governance, or other major DAMA DMBOK or industry-recognized data management practices.
Experience working with and defining data.
Experience with enterprise architecture frameworks, such as DoDAF or UAF.
Logical thinking and the ability to articulate thoughts verbally and diagrammatically.

Responsibilities

Analyze operational activities to establish a quantitative, rational basis for decision-making and resource allocation.
Understand and explain complex topics related to data management, data governance, and data maturity to stakeholders and leadership.
Work with subject matter experts across organizations to collect and update business and technical metadata.
Reverse-engineer data models and other design or build artifacts to extract relevant business and technical metadata and load it into a metadata repository.
Review submitted data to identify data-quality problems and anomalies, and help correct current and historical data.
Collect and analyze data and associated system artifacts for stakeholders to use in improving business practices.
Analyze how data might best be applied to benefit stakeholders.
Develop ETL routines to load and curate disparate data sets.
Explore and develop AI proofs of concept related to data management and enterprise architecture.
Document AI use case journeys so they can be communicated to different audiences and their limitations understood.