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Generative Ai Developer Jobs in North Carolina (NOW HIRING)

XPath Solutions

Generative AI Engineer

Charlotte, NC · On-site

$140 - $190/hr

Charlotte, United States | Posted on 07/15/2026 Dallas, TX or Charlotte, NC or Raleigh, NC Role Overview We are seeking a highly skilled Generative AI Engineer with a strong Python background to ...

Saransh Inc

Generative AI Architect

Charlotte, NC · On-site

$61.50 - $81/hr

Generative AI Architect Location: Charlotte, NC (Onsite from Day 1) Job Type: Contract Skill ... and prompt engineering frameworks 3. Participate in cross-functional GenAI initiatives and PoC ...

Apex Systems

Senior Generative AI Engineer

Charlotte, NC · On-site

$102K - $140K/yr

Job#: 3045280 Senior Generative AI Engineer Location: Charlotte, North Carolina (Onsite) Role ... Experience with CI/CD platforms like Jenkins, GitHub Actions, GitLab CI, or Azure DevOps. * ...

TIAA

Generative AI Technical Lead

Charlotte, NC · On-site

$184K - $304K/yr

This Generative AI Technical Lead will drive the design, development, and delivery of enterprise AI ... This will sit at the intersection of business and engineering, translating stakeholder pain points ...

Euclid Innovations

Senior Generative AI Engineer

Charlotte, NC · On-site

$102K - $140K/yr

They are seeking a Senior Generative AI Engineer to design, integrate, and operate core AI platform ... DevOps. Founded in 2009, the company is headquartered in Charlotte, USA, with a team of 501-1000 ...

Judge Group, Inc.

Software Engineer

Charlotte, NC · On-site

$69 - $74/hr

Senior AI Developer, Generative AI (Full Stack) We are not accepting C2C or 1099 arrangements. Location: Charlotte, NC (Hybrid: 3 days onsite, 2 days remote) Schedule: Monday-Friday, 8:00 AM - 5:00 ...

GEI Consultants

AI Engineer

Cary, NC · On-site

$110K - $150K/yr

Experience with Generative AI Large Language Models (LLMs), including solution development and fine-tuning for domain-specific tasks. * Proficiency in at least one programming language such as Python ...

ApTask

.Net Developer with AI

Charlotte, NC · On-site

NET Full Stack Developer with expertise in Angular , Generative AI , LangChain, and LangGraph to design, develop, and implement intelligent web applications. The ideal candidate will have strong ...

GEI Consultants

AI Engineer

Cary, NC · On-site

Experience with Generative AI Large Language Models (LLMs), including solution development and fine-tuning for domain-specific tasks. * Proficiency in at least one programming language such as Python ...

Scout Motors

Lead, AI Engineering

Charlotte, NC · On-site

$100K - $131K/yr

The team combines expertise in machine learning, generative AI, data engineering, and platform infrastructure to deliver innovative solutions that improve business outcomes and accelerate digital ...

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Generative Ai Developer information

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How much do generative ai developer jobs pay per hour?

As of Aug 25, 2026, the average hourly pay for generative ai developer in North Carolina is $41.16, according to ZipRecruiter salary data. Most workers in this role earn between $21.39 and $49.81 per hour, depending on experience, location, and employer.

What is a generative AI developer?

A Generative AI Developer is a technology professional who specializes in designing, building, and deploying artificial intelligence systems that can create new content, such as text, images, audio, or code. They work with advanced machine learning models, like generative adversarial networks (GANs) or large language models, to enable computers to produce original outputs. These developers often collaborate with data scientists, researchers, and product teams to integrate AI-generated content into software applications and business solutions.

What are the key skills and qualifications needed to thrive as a generative AI developer?

To thrive as a Generative AI Developer, you need strong programming skills (especially in Python), a deep understanding of machine learning concepts, and an advanced degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and experience with cloud platforms or model deployment tools are typically required. Creative problem-solving, adaptability, and effective collaboration are standout soft skills in this evolving field. These abilities are crucial to design, implement, and refine generative models that solve real-world problems and drive innovation.

What are some common challenges faced by generative AI developers when deploying models in production environments?

Generative AI Developers often encounter challenges such as ensuring model reliability, managing computational resource requirements, and addressing ethical considerations like data bias or content safety. Deploying generative models at scale requires robust monitoring to detect unexpected outputs or model drift, and collaboration with data engineers and product teams to optimize performance. Staying up-to-date with evolving frameworks and best practices is essential, as production environments demand both technical rigor and adaptability to new AI advancements.

What is the difference between Generative Ai Developer vs Machine Learning Engineer?

AspectGenerative Ai DeveloperMachine Learning Engineer
CredentialsBachelor's or higher in CS, AI, or related fields; experience with deep learning frameworksBachelor's or higher in CS, Data Science, or related fields; strong programming skills
Work EnvironmentDevelops AI models for content creation, chatbots, and creative applicationsBuilds and deploys ML models for various data-driven solutions across industries
Industry UsageTech, entertainment, marketing, and creative sectorsFinance, healthcare, tech, and e-commerce sectors

While both roles involve AI and machine learning, Generative Ai Developers focus on creating models that generate content, such as images or text, whereas Machine Learning Engineers develop broader ML solutions for diverse applications. The roles often overlap but differ mainly in their specific focus areas and use cases.

How to become a generative AI developer?

To become a generative AI developer, you should have a strong foundation in programming languages like Python, experience with machine learning frameworks such as TensorFlow or PyTorch, and knowledge of neural network architectures like transformers. Gaining expertise in natural language processing and deep learning, along with practical experience through projects or internships, is essential. Certifications in AI or data science can also enhance your qualifications.

What are popular job titles related to Generative Ai Developer jobs in North Carolina?

For Generative Ai Developer jobs in North Carolina, the most frequently searched job titles are:

What job categories do people searching Generative Ai Developer jobs in North Carolina look for?

The top searched job categories for Generative Ai Developer jobs in North Carolina are:

What cities in North Carolina are hiring for Generative Ai Developer jobs?

Cities in North Carolina with the most Generative Ai Developer job openings:

Generative Ai Developer jobs near you
Infographic showing various Generative Ai Developer job openings in North Carolina as of August 2026, with employment types broken down into 81% Full Time, 17% Part Time, and 2% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $85,609 per year, or $41.2 per hour.

Generative AI Engineer

XPath Solutions

Charlotte, NC • On-site

$140 - $190/hr

Other

Re-posted 10 days ago

Job description

Charlotte, United States | Posted on 07/15/2026

Dallas, TX or Charlotte, NC or Raleigh, NC

Role Overview

We are seeking a highly skilled Generative AI Engineer with a strong Python background to design, develop, and deploy cutting‑edge AI solutions. The ideal candidate will have hands‑on experience with Large Language Models (LLMs), Vision Language Models (Vision LLMs/VLMs), vLLM inference framework, prompt engineering, and modern Generative AI frameworks, along with proven expertise in building scalable AI applications for enterprise use cases.

This role focuses on developing Agentic AI systems, Retrieval‑Augmented Generation (RAG), multimodal AI solutions, and high‑performance LLM inference while integrating GenAI capabilities into production‑grade enterprise applications.

Mission

Design and deliver scalable, production‑ready Generative AI solutions leveraging modern LLMs, Vision LLMs, Agentic AI frameworks, RAG architectures, and cloud AI platforms to power intelligent enterprise applications.

Key Responsibilities Design and implement Generative AI solutions for:
  • Text‑based AI applications
  • Image‑based AI applications
  • Multimodal AI applications
  • Develop and optimize advanced prompt engineering strategies to improve LLM performance, accuracy, and reliability.
  • Build and integrate embedding‑based retrieval systems and Retrieval‑Augmented Generation (RAG) pipelines.
Design and implement Agentic AI applications including:
  • Context management
  • Session and memory handling
  • Tool calling and workflow orchestration
  • Deploy and optimize vLLM for high‑throughput, low‑latency LLM inference in production environments.
  • Build scalable APIs using Python and integrate GenAI capabilities into enterprise applications and workflows.
  • Collaborate with cross‑functional teams to deploy AI solutions at scale.
  • Ensure AI solutions are secure, scalable, reliable, and production‑ready.
Required Qualifications Programming
  • Strong proficiency in Python

Solid experience with AI/ML frameworks including:

  • PyTorch
  • TensorFlow
Agentic AI

Hands‑on experience building multi‑agent AI systems, including:

  • Session management
  • Memory handling
  • Tool integration and orchestration

Practical experience with:

  • vLLM for optimized LLM serving and inference
Retrieval & Search

Experience with:

  • Embeddings
  • Retrieval‑Augmented Generation (RAG)
  • Semantic Search

Experience with one or more:

  • AWS SageMaker
MLOps
  • Understanding of MLOps and LLMOps practices
  • Experience deploying scalable AI applications in production
Preferred Qualifications
  • Experience with multimodal AI systems combining text, images, and documents

Knowledge of AI ethics, including:

  • Responsible AI practices
  • Experience designing AI systems with governance, transparency, and compliance in mind
  • Experience with distributed GPU inference, model optimization, quantization, and high‑performance AI serving
  • Familiarity with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen
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