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About This Role
Job Title: AI Prompt Engineer
Reports To: TBD
FLSA Status: Exempt
Position Summary
Nassau Candy is seeking an experienced AI Prompt Engineer to help drive the organization's artificial intelligence strategy by designing, implementing, and optimizing AI\-powered business solutions. This individual will work closely with business leaders across manufacturing, distribution, sales, customer service, finance, HR, and other functional areas to identify opportunities where artificial intelligence can improve efficiency, automate repetitive tasks, enhance decision\-making, and support our NetSuite ERP environment.
The ideal candidate combines strong technical expertise with practical business acumen and has hands\-on experience developing AI prompts, intelligent agents, workflow automations, and system integrations. This position will play a critical role in accelerating Nassau Candy's digital transformation initiatives while ensuring solutions are secure, scalable, accurate, and aligned with company standards.
Essential Duties and Responsibilities
AI Strategy \& Business Process Improvement
- Identifyopportunities toleverageAI across business operations to improve productivity, quality, and operational efficiency.
- Partner with department leaders to understand business processes and recommend AI\-driven improvements.
- Evaluate existing workflows and develop automation solutions that reduce manual effort and eliminate repetitive tasks.
- Support continuous improvement initiatives through intelligent process automation.
Prompt Engineering \& AI Development
- Design, test, andoptimizeprompts for Large Language Models (LLMs) to deliveraccurate, consistent, and business\-appropriate outputs.
- Develop andmaintainAI agents, copilots, and intelligent assistants that support internal users.
- Continuously evaluate AI performance and refine prompts based on user feedback, business requirements, and measurable outcomes.
- Establish prompt engineering standards and best practices across the organization.
Solution Development \& Systems Integration
- Develop AI\-powered tools, automations, and workflow solutions using Python, APIs, scripting, and integration technologies.
- Integrate AI capabilities into existing business applications, including NetSuite and other enterprise platforms.
- Connect internal and external data sources to enable end\-to\-end intelligent workflows.
- Collaborate with IT, software vendors, and business stakeholders on solution architecture and deployment.
Analytics \& Performance Measurement
- Develop key performance indicators (KPIs) to measure AI effectiveness, including:
- Accuracy
- Productivity improvements
- Cycle time reduction
- Workflow throughput
- User adoption
- Operational efficiency
- Analyze AI performance and recommend ongoing improvements.
- Prepare reports and presentationsdemonstratingbusiness impact and return on investment.
AI Governance \& User Adoption
- Develop documentation, standards, and governance surrounding responsible AI usage.
- Train employees on AI tools and best practices.
- Partner with leadership to encourage adoption of AI solutions throughout the organization.
- Ensure AI implementationscomply withcompany security standards, privacy requirements, and internal policies.
Qualifications
Required
- Bachelor's degree in Computer Science, Information Systems, Software Engineering, Data Science, Artificial Intelligence, or a related technical field, or equivalent combination of education and experience.
- Five (5\) or more years of experience in software engineering, automation, systems integration, or technical solution development.
- Demonstrated experience implementing AI or Large Language Model (LLM) solutions in production business environments.
- Strong programming skills, including Python and API development.
- Experience with scripting, automation frameworks, and workflow development.
- Experience integrating enterprise applications and business systems.
- Strong analytical, troubleshooting, and problem\-solving skills.
- Excellent communication skills with the ability to translate technical concepts for non\-technical stakeholders.
- Ability to manage multiple projects and priorities in a fast\-paced environment.
Preferred
- Experience with NetSuite ERP.
- Experience with Microsoft Copilot, OpenAI, Anthropic Claude, Google Gemini, or similar enterprise AI platforms.
- Experience with workflow automation platforms such as Power Automate, Make, Zapier, or n8n.
- Experience building AI agents and orchestration workflows.
- Experience supporting manufacturing, distribution, supply chain, wholesale, consumer products, or food manufacturing environments.
- Familiarity with SQL, cloud platforms, and enterprise integration architecture.
Knowledge, Skills \& Abilities
- Strong understanding of prompt engineering methodologies and AI optimization techniques.
- Knowledge of Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), embeddings, and AI workflow architecture.
- Ability to design scalable AI solutions that improve operational performance.
- Strong project management and organizational skills.
- Excellent written and verbal communication abilities.
- Ability to work independently while collaborating effectively across departments.
- Commitment to continuous learning in the rapidly evolving AI landscape.
Physical Requirements
- Ability to remain seated for extended periods while working at a computer.
- Ability to occasionallylift upto 20 pounds.
- Ability to travel occasionally between Nassau Candy facilities as business needs require.
Working Conditions
- Corporate office environment with periodic visits to warehouse, manufacturing, and distribution facilities.
- Occasional evening or weekend work may berequiredto support projects or system implementations.
- Hybrid workschedulemay be available based on business needs.
What Success Looks Like
A successful AI Prompt Engineer will:
- Develop and deploy AI solutions that significantly improve business productivity and reduce manual work.
- Successfully integrate AI capabilities into key Nassau Candy business processes and enterprise systems.
- Establish measurable improvements in workflow efficiency, response quality, and operational performance.
- Increase employee adoption of AI tools through effective training and user\-friendly solutions.
- Build scalable AI standards, documentation, and governance that support long\-term organizational growth.
- Serve as a trusted technical advisor helping Nassau Candy responsibly expand the use of artificial intelligence across the enterprise.
Salary Context
This $120K-$180K range is above the 75th percentile for Prompt Engineer roles in our dataset (median: $127K across 5 roles with salary data).
View full Prompt Engineer salary data →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 3,708 AI roles we're tracking, Prompt Engineer positions make up 0% of the market. At Nassau Candy, 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 $140,000 based on 11 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($150K) sits 7% above the category median. Disclosed range: $120K to $180K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Nassau Candy AI Hiring
Nassau Candy has 1 open AI role right now. They're hiring across Prompt Engineer. Based in Hicksville, NY, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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