Generative AI Engineer

XPath Solutions; Charlotte (NC); On-site; USD 140,000 - 190,000; Full time

Job summary

XPath Solutions is seeking a Generative AI Engineer in the Charlotte area to design, develop, and deploy enterprise Generative AI solutions. The role focuses on LLMs, Vision LLMs, vLLM inference, prompt engineering, RAG, and multimodal AI capable of production-grade deployments.

You will implement Agentic AI systems, manage sessions/memory, enable tool calling, and build scalable Python APIs to integrate GenAI into enterprise workflows while ensuring security and reliability.

Qualifications

• Strong proficiency in Python and modern AI frameworks.
• Experience with PyTorch and TensorFlow for large-scale models.
• Hands-on with vLLM inference and high-throughput serving.
• Familiarity with LLMs, Vision LLMs, and multimodal AI concepts.
• Experience building RAG pipelines and embedding-based retrieval.

Responsibilities

• Design and implement Generative AI solutions for text, image, and multimodal applications.
• Develop and optimize prompt engineering strategies to improve LLM performance, accuracy, and reliability.
• Build embedding-based retrieval systems and RAG pipelines.
• Develop Agentic AI applications: context management, memory handling, tool integration, and workflow orchestration.
• Deploy vLLM in production for high-throughput, low-latency inference.
• Build scalable Python APIs and integrate GenAI capabilities into enterprise workflows.
• Collaborate with cross-functional teams to deploy AI at scale, ensuring security and reliability.
• Explore and implement multi-agent AI systems and governance practices.

Skills

Python

PyTorch

TensorFlow

vLLM

Prompt engineering

Large Language Models

Vision LLMs

RAG

Embeddings

Agentic AI

Multimodal AI

AWS SageMaker

LangChain

LangGraph

LlamaIndex

CrewAI

AutoGen

API design

Tools

Python

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