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About This Role
Artificial Intelligence Solutions Developer Intern
Company: LOUI Consulting Group, Inc. (LCGI)
Location: 114 Constitution Drive, Suite 200, Warner Robins, GA 31088
Position Type: Paid Internship
Compensation: Starting at $12\.00 per hour
Schedule: Approximately 20 hours per week with opportunity for additional hours based on project needs
Employment Term: Year\-round internship (Fall, Spring, and Summer)
Work Environment: In\-person
About LOUI Consulting Group
LOUI Consulting Group (LCGI) is a technology and consulting company focused on solving real\-world business challenges through Artificial Intelligence, automation, software innovation, and data\-driven decision making. Our projects span multiple industries including healthcare, aviation, nonprofit organizations, government contracting, and commercial software development.
Unlike traditional internships that focus on a single product or technology, our interns gain experience working across multiple active businesses and client initiatives. Every day presents an opportunity to solve new challenges, evaluate emerging AI technologies, and help build innovative software solutions.
Position Summary
LCGI is seeking an energetic, curious, and self\-motivated Artificial Intelligence Solutions Developer Intern to join our growing innovation team.
This internship is designed for individuals who are excited about the future of Artificial Intelligence and want hands\-on experience applying AI technologies to solve real business problems. Rather than spending months writing software from scratch, interns will learn how to leverage modern AI tools, no\-code and low\-code platforms, workflow automation, and intelligent software development practices to rapidly prototype and deploy solutions.
The successful candidate will work directly with company leadership and participate in projects supporting multiple businesses and technology initiatives.
Essential Responsibilities
The Artificial Intelligence Solutions Developer Intern will:
- Research emerging Artificial Intelligence technologies and evaluate their business applications.
- Design and develop AI\-powered solutions using modern no\-code and low\-code development platforms.
- Build prototypes, production applications, and internal automation tools.
- Assist in developing AI agents and intelligent workflow automation.
- Evaluate new AI software platforms and recommend business applications.
- Participate in brainstorming sessions to identify opportunities for automation and innovation.
- Assist with website enhancements and digital transformation initiatives.
- Research industry trends and prepare executive summaries.
- Document software solutions, workflows, and technical processes.
- Participate in meetings with company leadership and subject matter experts.
- Attend selected client meetings and presentations as part of the project team.
- Present new AI tools, technologies, or workflow improvements during weekly innovation meetings.
Learning Opportunities
Interns will gain practical experience with:
- Artificial Intelligence business applications
- Prompt engineering
- AI\-assisted software development
- Workflow automation
- Business process improvement
- API integrations
- Website development
- Product management
- Technology research
- Client presentations
- Innovation management
- Cross\-industry technology consulting
Training will be provided through one\-on\-one mentoring with experienced AI professionals.
Prior AI development experience is not required.
Minimum Qualifications
Applicants should possess:
- High school diploma or equivalent (college students preferred)
- Enrollment in or interest in a technical field of study
- Minimum cumulative GPA of 2\.5
- Basic understanding of HTML
- Strong computer skills
- Excellent written and verbal communication skills
- Strong organizational skills
- Ability to learn new technologies independently
- Authorization to work in the United States (U.S. Citizens only)
Preferred Qualifications
Preference will be given to candidates who have experience with one or more of the following:
- SQL
- Website development
- Artificial Intelligence tools
- Prompt engineering
- Workflow automation
- Technology research
- Software testing
- GitHub
- Personal technology projects
Programming experience is welcomed but not required.
Desired Characteristics
We are looking for someone who is:
- Curious
- Energetic
- Self\-motivated
- A strong listener
- Creative
- Detail\-oriented
- Professional
- Adaptable
- Entrepreneurial
- Passionate about learning
The ideal candidate enjoys solving problems and continuously exploring new technologies.
Our AI Development Philosophy
At LCGI, we believe the future belongs to professionals who understand how to combine human creativity with Artificial Intelligence.
Our interns are encouraged to:
- Think like entrepreneurs.
- Learn continuously.
- Build rapidly.
- Automate repetitive work.
- Focus on solving business problems.
- Use AI responsibly.
- Communicate clearly.
- Maintain the highest ethical standards.
Weekly Expectations
Interns are expected to:
- Research emerging AI technologies.
- Present one new AI tool, workflow, or innovation to the team each week.
- Maintain an AI Opportunity Log documenting ideas that could improve one of LCGI's businesses.
- Demonstrate continuous learning through experimentation with new AI platforms.
- Participate in project meetings and brainstorming sessions.
- Complete assigned project milestones on schedule.
Professional Development
Each intern will receive:
- One\-on\-one mentoring
- Project\-based learning
- Direct exposure to executive leadership
- Opportunities to attend client presentations
- Portfolio development opportunities
- GitHub project experience
- Exposure to multiple industries and business models
Internship Deliverables
Before completing the internship, participants will be expected to:
- Build a professional portfolio of completed projects.
- Contribute to one or more production or prototype software applications.
- Maintain a GitHub repository of completed work, where appropriate.
- Deliver presentations demonstrating AI solutions developed during the internship.
- Document lessons learned and recommendations for future projects.
Future Opportunities
Outstanding interns may be considered for:
- Continued internship employment
- Independent contractor opportunities
- Part\-time employment while completing school
- Full\-time employment upon graduation
Why Join LCGI?
This internship is ideal for students who want more than routine software development.
Instead of working on a single application, you will help solve real\-world challenges across healthcare, aviation, nonprofit organizations, software startups, digital marketing, and Artificial Intelligence innovation. You will work directly with experienced business leaders while gaining practical experience using cutting\-edge AI technologies to create meaningful business solutions.
If you are excited about the future of Artificial Intelligence and want to help build it, we encourage you to apply or send your resume directly to Mr. Reginald Adams, [email protected].
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LOUi Consulting, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
LOUi Consulting AI Hiring
LOUi Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Warner Robins, GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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