Generative AI Engineer

Job Description

Job Description

Generative AI Engineer
Location

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
  
• Vision Language Models (Vision LLMs)
  
• Multimodal AI applications
  

AI Engineering

• 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
    
  • MCP (Model Context Protocol)
    
  • 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
  

AI / Machine Learning

• Solid experience with AI/ML frameworks including:
  

  • PyTorch
    
  • TensorFlow
    

Agentic AI

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

• Session management
  
• Memory handling
  
• MCP (Model Context Protocol)
  
• Tool integration and orchestration
  

Large Language Models

Practical experience with:

• Large Language Models (LLMs)
  
• Vision Language Models (Vision LLMs / VLMs)
  
• Transformer architectures
  
• Hugging Face ecosystem
  
• vLLM for optimized LLM serving and inference
  

Retrieval & Search

Experience with:

• Vector databases
  
• Embeddings
  
• Retrieval-Augmented Generation (RAG)
  
• Semantic Search
  

Cloud AI Platforms

Experience with one or more:

• AWS SageMaker
  
• Azure OpenAI
  
• Google Vertex AI
  

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:
  

  • Bias mitigation
    
  • 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