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About This Role
AI Consultant / Full\-Stack Developer – Online Training \& AI Solutions
Location: Rapid City, South Dakota (On\-Site Only)
Position Type: Full\-Time, Salaried
Please do not apply if you are seeking remote or hybrid work. This position requires working in person in Rapid City, South Dakota.
Job Overview
We are seeking a highly motivated and innovative AI Consultant / Full\-Stack Developer with strong AI, coding, and web development skills to help build an innovative AI consulting and online training business. This is an exciting opportunity to work with cutting\-edge artificial intelligence technologies while developing AI\-powered business solutions, online training programs, and modern web applications.
This position goes far beyond creating online courses. You will help design and develop AI\-powered learning experiences, build and manage websites from concept through deployment, develop custom AI solutions for clients, and play a key role in launching and growing an AI consulting business. For the right individual, this position offers significant opportunities for professional growth, leadership, and revenue sharing as the business expands.
Responsibilities
- Design and develop engaging online training courses covering AI concepts, tools, and real\-world applications.
- Translate complex AI and machine learning concepts into clear, practical learning experiences for diverse audiences.
- Design, build, deploy, and maintain websites from end to end.
- Develop AI\-powered applications, automations, and business solutions for clients.
- Create interactive SCORM\-compliant training modules for Learning Management Systems.
- Develop AI scenarios, simulations, assessments, and interactive learning activities.
- Manage and optimize Learning Management Systems using reporting, analytics, gamification, and adaptive learning features.
- Collaborate with subject matter experts and instructional designers to produce high\-quality educational content.
- Research emerging AI technologies and recommend innovative solutions for clients and training programs.
- Continuously improve training materials and AI solutions based on analytics and user feedback.
- Ensure all websites, applications, and training materials meet accessibility, usability, security, and industry standards.
- Assist in developing new consulting services, products, and business opportunities as the company grows.
Qualifications
- Strong experience using modern AI platforms and tools such as ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, or similar technologies.
- Proficiency in programming languages such as Python, JavaScript, TypeScript, HTML, CSS, SQL, or related technologies.
- Experience designing, developing, deploying, and maintaining websites from end to end.
- Experience with modern web development frameworks is preferred.
- Experience working with Learning Management Systems such as Moodle, Canvas, Blackboard, TalentLMS, or similar platforms.
- Experience with e\-learning authoring tools such as Articulate Storyline, Rise, Adobe Captivate, or similar software is preferred.
- Strong written and verbal communication skills with the ability to explain technical concepts to non\-technical audiences.
- Excellent analytical, organizational, and problem\-solving skills.
- Ability to manage multiple projects in a fast\-paced environment.
- Self\-motivated, innovative, and eager to stay current with rapidly evolving AI technologies.
- Entrepreneurial mindset with an interest in helping build and grow a technology\-focused consulting business.
What We Offer
- Full\-time salaried position with competitive compensation based on experience.
- Opportunity for revenue sharing as the business grows.
- Long\-term career growth with leadership opportunities in an expanding AI consulting business.
If you are passionate about artificial intelligence, software development, website management, and building innovative technology solutions, we would love to hear from you. Join us in helping businesses and organizations leverage AI while creating industry\-leading online training experiences.
This is a full\-time, on\-site position located in Rapid City, South Dakota. Remote or hybrid applicants will not be considered.
Pay: $60,000\.00 \- $75,000\.00 per year
Work Location: In person
Salary Context
This $60K-$75K range is in the lower quartile for AI Consultant roles in our dataset (median: $130K across 5 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Consultant positions make up 0% of the market. At Precision Z, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Consultant roles pay a median of $152,604 based on 12 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($67K) sits 56% below the category median. Disclosed range: $60K to $75K.
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.
Precision Z AI Hiring
Precision Z has 1 open AI role right now. They're hiring across AI Consultant. Based in Rapid City, SD, US. Compensation range: $75K - $75K.
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 AI Consultant roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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