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About This Role
AI BUSINESS DEVELOPMENT REPRESENTATIVE (BDR)High Commission \+ Bonuses \+ Advancement Opportunities
Location: Remote / HybridCompensation: Commission \+ Performance BonusesIndustry: Artificial Intelligence \| Automation \| Robotics \| Business Technology
ABOUT NEIL AI GLOBAL SOLUTIONS
Neil AI Global Solutions is one of the fastest\-growing AI automation and robotics companies in the Southeast. We help businesses increase efficiency, reduce operational costs, improve customer experiences, and scale revenue through cutting\-edge Artificial Intelligence, Automation Software, AI Employees, Voice Agents, and Robotics Solutions.
We are looking for ambitious professionals who are serious about building a career in technology sales and want to represent solutions that are transforming the future of business.
If you are highly motivated, coachable, competitive, and enjoy speaking with business owners and executives, this opportunity offers unlimited income potential and career growth.
POSITION SUMMARY
As an AI Business Development Representative, you will engage with business owners, executives, and decision\-makers to identify operational challenges and introduce AI\-driven solutions that help organizations grow, automate, and become more profitable.
Your primary responsibility is generating qualified appointments and sales opportunities for our AI and automation specialists.
WHAT YOU WILL DO
- Contact business owners, executives, and decision\-makers through outbound prospecting Follow up with inbound leads and marketing\-generated opportunities Identify business challenges and uncover automation opportunities Schedule qualified discovery meetings and product demonstrations Present the value of AI software, automation platforms, AI voice agents, and robotics solutions Maintain accurate CRM records and sales activity Build relationships with prospects and nurture opportunities through the sales process Meet and exceed weekly and monthly performance goals Collaborate with leadership and sales teams to maximize conversions
WHO WE ARE LOOKING FOR
- Strong communication and relationship\-building skills Confident and professional phone presence Self\-motivated and goal\-oriented mindset Ability to handle objections and maintain a positive attitude Comfortable speaking with business owners and executives Organized, disciplined, and consistent with follow\-up Coachable and willing to learn AI, automation, and technology solutions
PREFERRED EXPERIENCE
- B2B Sales Telemarketing Appointment Setting Business Development SaaS Sales Technology Sales CRM Experience (GoHighLevel, Salesforce, HubSpot, etc.)
WHAT WE OFFER
- Unlimited earning potential High commission structure Performance bonuses Sales training and mentorship AI and technology sales education Leadership advancement opportunities Flexible work schedule Opportunity to represent one of the most exciting industries in the world
IDEAL CANDIDATE
The ideal candidate is hungry, competitive, professional, and committed to personal growth. You understand that success is earned through consistent activity, discipline, and strong communication skills.
You are not looking for a job.
You are looking for an opportunity.
If you believe you were made for more and want to help businesses automate, elevate, and dominate through AI technology, we want to hear from you.
COMPANY MISSION
At Neil AI Global Solutions, we believe technology should empower people—not replace them.
Our mission is to help businesses work smarter, serve customers better, and create more opportunities through Artificial Intelligence and Automation.
Apply today and become part of the future of business technology.
Pay: $2,517\.65 \- $6,236\.31 per month
Benefits:
- Dental insurance
- Employee discount
- Flexible schedule
- Health insurance
- Vision insurance
- Work from home
Experience:
- Telemarketing: 1 year (Required)
Language:
- English (Required)
- Spanish (Preferred)
Work Location: Remote
Salary Context
This $30K-$74K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Neil Al Global Solutions, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($52K) sits 71% below the category median. Disclosed range: $30K to $74K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Neil Al Global Solutions AI Hiring
Neil Al Global Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Greensboro, NC, US, Remote, US. Compensation range: $74K - $200K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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