Dallas, TX (or) Charlotte, NC (or) Raleigh, NC
US Citizens and Green Card Holders needed
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) , prompt engineering , and 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), and multi modal AI solutions while integrating GenAI capabilities into production-grade applications and workflows.
Design and deliver scalable, production-ready Generative AI solutions that leverage modern LLMs, agentic frameworks, and cloud AI platforms to power intelligent applications across the enterprise.
Design and implement Generative AI models for:
Text-based applications
Image-based applications
Multimodal AI solutions
Develop and optimize prompt engineering strategies to improve LLM performance and reliability
Build and integrate embedding-based retrieval systems and RAG pipelines
Integrate Generative AI capabilities into web applications and enterprise workflows
Design and develop agentic AI applications with:
Context management
Session and memory handling
MCP (Model Context Protocol) tools
Collaborate with cross-functional teams to deploy AI solutions at scale
Ensure AI solutions are reliable, secure, and production-ready
Strong proficiency in Python
Solid experience with AI/ML frameworks such as:
PyTorch
TensorFlow
Hands-on experience building multi-agent systems , including:
Session management
Memory handling
MCP tools
Practical experience working with:
Large Language Models (LLMs)
Transformer architectures
Hugging Face ecosystem
Knowledge and experience with:
Vector databases
Retrieval-Augmented Generation (RAG)
Semantic search techniques
Familiarity with cloud AI services , including:
AWS SageMaker
Azure OpenAI
GCP Vertex AI
Understanding of MLOps practices for scalable AI deployment
Knowledge of AI ethics , including:
Bias mitigation
Responsible AI practices
Experience designing AI systems with governance, transparency, and compliance in mind
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