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About This Role
AI Instructor / Trainer – Business AI, Automation & AI Strategy
Aspire Training and Development Independent Contractor | $125–$200 per instructional hour Fully Remote
About Aspire Training and Development: www.aspiretraininganddevelopment.com
Aspire Training and Development provides practical, high-impact professional development programs that help organizations strengthen their people and build internal capabilities.
We are expanding our AI training offerings and are seeking experienced AI instructors to deliver our AI Champions Program and advanced AI workshops to business professionals, managers, leaders, and internal AI Champions.
About the Opportunity
We are seeking experienced AI instructors, practitioners, consultants, and technology professionals who can deliver engaging, practical training that helps organizations apply AI to real business opportunities.
Our programs are designed primarily for business professionals rather than software engineers. Instructors must be able to take potentially complex AI concepts and make them accessible, practical, and relevant to participants working in areas such as sales, marketing, operations, HR, finance, customer service, and management.
We offer nine AI courses covering business applications, advanced AI use, automation, agents, internal applications, governance, strategy, and implementation.
We do not expect every instructor to be qualified to teach all nine courses. Instructors will be selected for courses based on their areas of expertise, training experience, and availability. Candidates who are qualified to teach multiple courses may have opportunities to deliver multiple programs.
Training will be delivered primarily online, with some opportunities for in-person delivery in Massachusetts.
AI Champions Core Program
1. AI for Business: Opportunities, Applications & Action Explore practical, industry-specific applications of AI involving revenue growth, productivity, automation, data analysis, customer engagement, and decision-making.
2. AI Champions I: Using AI to Work Smarter Develop participants into advanced AI users through effective use of LLMs, information analysis, repeatable workflows, and practical workplace applications.
3. AI Champions II: Building with AI Move from using AI to building with it through workflow automation, AI agents, integrations, data analysis, APIs, and lightweight application development.
4. AI Action Workshop: From Ideas to Implementation Help organizations evaluate and prioritize AI opportunities based on business impact, feasibility, effort, risk, and resources and develop a focused implementation plan.
Advanced AI Workshops
5. AI Governance & Responsible Use Develop practical approaches to AI governance, data privacy, security, acceptable use, human oversight, risk, and responsible adoption.
6. Advanced AI Agents & Automation Explore multi-step AI agents and advanced automations that interact with business systems, information sources, and recurring workflows.
7. Building Internal AI Applications Help organizations turn successful prototypes into shared internal tools such as knowledge assistants, reporting applications, and AI-enabled workflows.
8. AI Leadership & Strategy Help organizational leaders identify where AI can create value, establish investment priorities, develop internal capabilities, and create a practical long-term AI strategy.
9. Working Effectively with AI Engineers & Developers Help business leaders and AI Champions recognize when technical expertise is required, translate business needs into requirements, work effectively with technical professionals, and move successful prototypes toward production.
Qualifications
We are interested in candidates who possess a combination of the following qualifications appropriate to the courses they will teach:
- Professional experience applying AI in business or organizational environments
- Strong working knowledge of generative AI and major LLM platforms
- Hands-on experience with tools such as ChatGPT/OpenAI, Claude, Gemini, Microsoft Copilot, or comparable platforms
- Experience with AI agents, workflow automation, data analysis, APIs, integrations, or application development
- Experience with automation platforms such as Make, Zapier, Power Automate, n8n, or comparable tools
- Understanding of common business applications such as Microsoft 365, Google Workspace, CRM platforms, and other business systems
- Ability to translate technical concepts into clear, practical language for nontechnical business professionals
- Strong presentation, facilitation, and communication skills
- Experience providing corporate training, consulting, workshops, or adult education strongly preferred
- Ability to customize training examples and exercises based on a client's industry, systems, and business priorities
- AI governance, cybersecurity, data governance, software development, or technology strategy experience desirable for applicable courses
We are particularly interested in practitioners who actively use and build with AI and can bring current, real-world experience into the classroom.
Compensation & Scheduling
$125–$200 per instructional hour, depending on course assignment, technical expertise, and experience.
The instructional rate is all-inclusive and covers:
- Preparation and curriculum organization
- Reasonable adaptation of the instructor’s existing materials
- Lesson plans, exercises, and digital handouts
Courses range from 2 hours to 4 hours and may be delivered as single sessions or over multiple sessions.
This is an independent contractor, project-based training position. Assignments will be scheduled based on client training needs, instructor expertise, and availability.
Candidates who demonstrate the ability to successfully deliver multiple courses may be offered additional training assignments.
Pay: $125.00 - $200.00 per hour
Benefits:
- Flexible schedule
Application Question(s):
- How would you describe your level of hands-on AI experience?
- Primarily an AI user
- Advanced/power user
- I build AI workflows and automations
- I build AI agents, integrations, or applications
- I professionally design or engineer AI-enabled systems
- What is your desired instructional hourly rate?
- Which of the following AI courses would you be interested in and qualified to teach? Please list the course numbers that apply (for example: 1, 2, 3, 6, 7).
- AI for Business: Opportunities, Applications & Action
- AI Champions I: Using AI to Work Smarter
- AI Champions II: Building with AI
- AI Action Workshop: From Ideas to Implementation
- AI Governance & Responsible Use
- Advanced AI Agents & Automation
- Building Internal AI Applications
- AI Leadership & Strategy
- Working Effectively with AI Engineers & Developers
- Are you currently based in the United States?
Yes / No
Experience:
- Adult education: 5 years (Preferred)
Work Location: Remote
Salary Context
This $260K-$416K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Aspire Training and Development, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($338K) sits 59% above the category median. Disclosed range: $260K to $416K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Aspire Training and Development AI Hiring
Aspire Training and Development has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $416K - $416K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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