Interested in this Prompt Engineer role at Diné Development Corporation?
Apply Now →Skills & Technologies
About This Role
The Prompt Engineer will work with a team to provide high-quality service to our customers in managing a portfolio of systems. This role is responsible for exploring and developing AI proofs of concept as they pertain to data management and enterprise architecture.
This position is a collaborative role that provides support for analysis and research into our team- and enterprise-wide processes. This position is also responsible for documenting AI use case journeys so that they can be adequately articulated to all audiences and so that limitations are understood.
The Prompt Engineer will work within an agile team to provide high-quality service to our customers by applying analytical techniques to determine solutions based on client requirements within an IT services/solutions scope.
They will analyze and reverse-engineer business and technical artifacts, such as data models, data catalogs, design documentation, and interfaces, to capture, store, and maintain relevant metadata for the systems within the portfolio.
Responsibilities:
- Analyze operational activities to obtain a quantitative, rational basis for decision making and resource allocation
- Conceptually understand and convey to stakeholders and leadership complex topics related to data management, data governance, and data maturity
- Work with subject matter experts (SME) 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 into a metadata repository
- Review submitted data to detect data quality problems and anomalies, and help the organization correct data: both current and historical
- Collects and analyzes data and associated system artifacts for stakeholders to use in perfecting their business practices
- Performs analysis to determine how data might best be applied to benefit the stakeholders
- Develops ETL routines to load and curate disparate data sets
Qualifications:
- Years of Experience: 3+
- Education Level: BS
- Clearance Requirements: Must be able to obtain a Secret Clearance
- Any other work-related qualifications needed for the role: + 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 communications + Experience with Agile development methodologies and roles
- Qualifications considered a plus + Experience using SQL for data analysis, data quality, and data profiling activities + 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 in developing RAG solutions + Experience with agentic development frameworks (e.g., Claude Code) + Experience in Information Management, including data design, data transformation, data governance or other major DAMA DMBOK or industry-recognized data management practice + Experience in working with and defining data + Experience with enterprise architecture frameworks (e.g., DoDAF, UAF) + Logical thinker; able to articulate thoughts both verbally and diagrammatically
About Us
Diné Development Corporation (DDC) is a Navajo Nation–owned family of companies that provides government agencies and commercial organizations with high-quality IT, professional, environmental, and research and development services. DDC is dedicated to empowering the Navajo Nation and the communities we serve.
Benefits
Eligible full-time employees receive a comprehensive benefits package, including medical, dental, vision, life and disability coverage, retirement savings with company match, and paid time off. Additional benefits include voluntary supplemental benefits, access to an employee assistance program, and educational assistance with tuition reimbursement.
Equal Opportunity & Compliance Statement
This contractor and subcontractor shall abide by the requirements of 41 CFR 60-1.4(a), 60-300.5(a), and 60-741.5(a). These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity, national origin, or for inquiring about, discussing, or disclosing information about compensation, or any other basis prohibited by law.
We participate in E-Verify.
DDC is committed to providing equal employment opportunities to all applicants and employees. If you require a reasonable accommodation to participate in the application or interview process, please contact Recruiting for assistance [email protected]
Role Details
About This Role
Prompt Engineers design, test, and optimize interactions with large language models. They build evaluation frameworks, craft system prompts, and develop techniques like chain-of-thought and few-shot learning to get consistent, reliable outputs. The role emerged alongside the GPT-3 era and has matured into a legitimate engineering discipline, not the 'just talk to the AI' job that early skeptics dismissed.
The work is more systematic than creative. You're running hundreds of prompt variations through evaluation suites, measuring output quality across edge cases, and building guardrails for production systems. When a prompt works 95% of the time but fails catastrophically on the other 5%, you need to find those failure modes and fix them before they hit users.
Across the 9,079 AI roles we're tracking, Prompt Engineer positions make up 0% of the market. At Diné Development Corporation, this role fits into their broader AI and engineering organization.
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
What the Work Looks Like
A typical week involves designing evaluation datasets for new use cases, benchmarking prompt strategies against each other with statistical rigor, working with product teams to define 'good enough' output quality, and building the tooling that lets non-technical teammates iterate on prompts safely. You'll spend more time in spreadsheets and evaluation dashboards than you'd expect.
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
Skills Required
The core requirement is deep LLM experience: prompt design, RAG architectures, and evaluation methodology. Python is table stakes. Many roles also want experience with specific providers like OpenAI, Anthropic, or open-source models. Understanding tokenization, context windows, and the practical differences between model families (reasoning ability, instruction following, output format compliance) separates strong candidates from the crowd.
Evaluation skills are becoming the differentiator. Can you design a rubric that measures output quality? Can you build automated evaluation pipelines? Do you understand when to use human evaluation vs. LLM-as-judge vs. deterministic checks? Companies are moving past 'vibes-based' prompt testing and want engineers who bring measurement discipline.
Strong postings specify the LLM use cases (summarization, extraction, classification, generation), the evaluation methodology they expect, and the production environment. Weak postings just say 'prompt engineering experience' without context. Look for companies that mention evaluation frameworks and production deployment.
Compensation Benchmarks
Prompt Engineer roles pay a median of $141,440 based on 27 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Diné Development Corporation AI Hiring
Diné Development Corporation has 1 open AI role right now. They're hiring across Prompt Engineer. Based in Dayton, OH, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Prompt Engineer roles include Technical Writer, NLP Researcher, Software Engineer.
From here, career progression typically leads toward AI Product Manager, LLM Engineer, AI Solutions Architect.
The best prompt engineers come from technical backgrounds and add LLM expertise, not the other way around. If you're coming from a non-technical role, invest heavily in Python, evaluation methodology, and understanding how LLMs work under the hood (tokenization, attention, context windows). The role will increasingly merge with LLM Engineering as the tools mature.
What to Expect in Interviews
Interviews focus on evaluation methodology and systematic thinking. You'll likely be asked to design a prompt for a specific use case, explain how you'd measure output quality, and walk through how you'd debug a prompt that works 90% of the time but fails on edge cases. Expect to discuss tokenization, context window management, and the tradeoffs between different prompting strategies (few-shot vs. chain-of-thought vs. tool use).
When evaluating opportunities: Strong postings specify the LLM use cases (summarization, extraction, classification, generation), the evaluation methodology they expect, and the production environment. Weak postings just say 'prompt engineering experience' without context. Look for companies that mention evaluation frameworks and production deployment.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.