Senior GenAI Engineer — Enterprise AI, Agents & RAG

Quantum Technologies. LLC; Plano (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Quantum Technologies LLC is seeking a hands-on Senior GenAI Engineer to build and deploy enterprise-scale Generative AI applications, AI agents, chatbots, and RAG-based solutions.

You will work with AWS Bedrock, LangChain, embeddings, and vector databases to deliver production-grade GenAI capabilities. The role emphasizes mentoring, scalable architecture, and secure deployments in a fast-paced environment.

Qualifications

• Hands-on experience with AWS Cloud Services and AWS Bedrock.
• Experience developing GenAI applications, AI agents, and chatbots.
• Strong experience implementing Retrieval-Augmented Generation (RAG) solutions.
• Data ingestion, document processing, embeddings, vector databases, and semantic search.
• Experience with LangChain, LangGraph, or similar AI agent frameworks.
• Knowledge of AI guardrails, safety controls, intent classification, and routing logic.
• Experience developing microservices, REST APIs, and distributed applications.
• Strong understanding of cloud-native architecture, security, scalability, and production deployments.
• Experience leading technical workstreams and mentoring junior engineers.

Responsibilities

• Lead the design, development, and deployment of enterprise-scale GenAI applications and AI agents.
• Build and maintain microservices and RESTful APIs to support GenAI workloads.
• Mentor junior engineers and guide technical workstreams to ensure high quality delivery.

Skills

AWS Cloud

AWS Bedrock

GenAI Apps

AI Agents

Chatbots

RAG Solutions

Data Ingestion

Embeddings

Vector DB

Semantic Search

LangChain

LangGraph

Guardrails

Routing Logic

Microservices

REST APIs

Cloud-Native

Security

Mentoring

Job description

Quantum Technologies LLC is seeking a hands-on Senior GenAI Engineer to build and deploy enterprise-scale Generative AI applications, AI agents, chatbots, and RAG-based solutions.

You will work with AWS Bedrock, LangChain, embeddings, and vector databases to deliver production-grade GenAI capabilities. The role emphasizes mentoring, scalable architecture, and secure deployments in a fast-paced environment.