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About This Role
SUMMARY OF THEJOB: Teaching faculty at Guilford Technical Community College are responsible for supporting student success by creating an optimum learning environment, responding to student needs, managing effective instructional activities, modeling employability skills, demonstrating professionalism, developing cooperative work relationships with other faculty and staff, supporting college administrative requirements, and maintaining competency in their instructional field. He/she will be responsible for quality instruction and for effective participation and interest in the total affairs of the college.
Under general supervision, this individual will prepare and teach courses designed to prepare students to utilize Artificial Intelligence (AI) more effectively, including the use of tools such as ChatGPT, Gemini, Microsoft Co-Pilot, etc. Areas of instructional emphasis include applied hands-on learning, AI Literacy and Responsible Use, Generative AI and Emerging Tools, and foundational concepts for clear application. The faculty member must be able to instruct a wide variety of topics within the curriculum in a classroom environment and may be required to teach day, evening and/or weekend hours. Flexibility to teach on any of GTCC multiple campuses (Greensboro, Jamestown, High Point or Cameron campus.) Duties/Functions ESSENTIALDUTIES/FUNCTIONS:
Teaching Prepare & teach departmental courses to include:
- developing learner centered lesson plans
- employing teaching strategies & instructional materials for different learning styles
- incorporating, as pedagogically appropriate, current technology in classroom, distance learning and laboratory environments
- creating and modeling a quality learning environment that supports a diverse student population
- preparing, distributing and utilizing instructional support materials, including course syllabi, supplementary materials, instructional media and other devices as appropriate
- conducting appropriate assessment of student learning outcomes in courses and programs/general education as appropriate
Professional Development Maintain a professional status that supports the instructional mission by:
- participating in professional development activities to maintain currency in field; maintaining current credentials or licensures as required by program or accreditation
- participating in professional development opportunities to advance teaching skills and strategies
Administration Provide daily & ongoing oversight of facilities, equipment and student records to include:
- maintaining classroom and laboratory spaces including upkeep of assigned equipment
- providing for the security of facilities, equipment and instructional materials and maintaining safe working conditions
- maintaining student records (e.g., grades and attendance) in accordance with established deadlines using a variety of technology-based programs (e.g., Colleague, Self Service, Canvas)
- complying with all applicable college, state and federal rules and regulations
Student Support Provide an environment conducive to student success to include:
- addressing student concerns in a timely manner
- promoting retention/persistence by assisting students to develop strategies for success
- referring students to campus and community resources when appropriate
- Demonstrate and model the College’s employability skills: adaptability, communication, information processing, problem solving, responsibility and teamwork.
Difficult Challenges Contacts Education Required Minimum requirements: o Bachelor’s degree in artificial intelligence, computer science, data science, computer information systems or a related field from an institutionally accredited college or university. o Experience in at least one programming language (e.g., Python), data literacy, machine learning and AI tools.
Education Preferred Preferred requirements: o Master’s degree in artificial intelligence, computer science, data science, computer information systems or a related field from an institutionally accredited college or university. o Current industry recognized certifications in any of the following: Google Pro Machine Learning Engineering, Microsoft Certified: Azure AI Engineer Associate, AWS Certified Machine Learning – Specialty, TensorFlow Developer Certificate, etc. Experience Required
Minimum requirements:*o Two years of professional experience in Artificial Intelligence, Machine Learning, Data Science or a related technical field. o Teaching and/or industry training experience in AI concepts including, Machine Learning Fundamentals, Data Analysis and Modeling, and AI Applications in any of the following formats: teaching courses, training sessions, conducting professional development workshops, and/or other continuing education seminars. Experience Preferred *Preferred requirements: o Greater than two years industry experiences applying AI in business, healthcare, education, or other applied domains. o Post secondary teaching experience. o Experience with assessment of student learning outcomes. o Experience with teaching distance learning (online) and/or alternate instructional delivery systems. KSA Required KNOWLEDGE, SKILLS & ABILITIES: The Instructor shall possess an understanding of and commitment to the nature and role of the Community College, particularly its “open door” policy. He/she shall have demonstrated personal and professional competence for the responsibilities assigned. The Instructor must be able to:
- Multi-task
- Supporting the vision and mission of the college
- Adapt to changing procedures, protocols or assignments
- Create and maintain a learner centered environment
- Communicate effectively
- Ability to effectively implement and apply technology solutions
KSA Preferred Department/Job Specific Requirements Mandatory GTCC Trainings (upon initial hire) and annual updates as required; other required trainings may be added as needed):
- Reporting Requirements
- Discrimination & Anti-Harassment (within 30 days of hire; annual refresher thereafter)
- Shooter on Campus (within 30 days of hire; annual refresher thereafter)
- Personal Information Protection (within 30 days of hire; annual refresher thereafter)
- Ethics and Social Responsibility (within 30 days of hire; annual refresher thereafter)
- Linking Student Learning Outcomes (within 30 days of hire)
- Canvas Certification (if teaching online or hybrid – within 30 days of hire)
Criminal history checks, with acceptable results, are required. Physical Demands
- May include teaching day and evening and on multiple campuses (Greensboro, Jamestown, High Point or Cameron campus.)
- Typical settings specific to discipline classroom or computer lab space.
- Physical Requirements
a. Stand for extended periods of time. b. Lift at least 25 pounds Posting Type Adjunct Faculty Posting Detail Information Posting Number 2026-016-TEMP-P
Open Date 09/29/2026
Close Date
Open Until Filled Yes
Special Instructions to Applicants
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Guilford Technical Community College, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Guilford Technical Community College AI Hiring
Guilford Technical Community College has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jamestown, NC, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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