AI Engineer: Build Scalable AI & LLM Solutions

Quantum Technologies. LLC; Indianapolis (IN); On-site; USD 120,000 - 180,000; Full time

Job summary

Quantum Technologies. LLC is seeking an experienced AI Engineer to design, develop, and deploy intelligent applications using AI, ML, and Generative AI.

You will build AI-powered solutions, develop predictive models, integrate large language models, and implement scalable AI systems. You will work with OpenAI, Azure OpenAI, and Hugging Face models, design retrieval-augmented generation pipelines, and optimize performance, latency, and reliability while deploying with Docker, Kubernetes, and

Qualifications

• Proficient in Python programming and ML/DL workflows.
• Experience with large language models and Generative AI.
• Hands-on with LangChain, LangGraph, or LlamaIndex.
• Experience with RAG architectures and vector databases.
• Familiar with REST APIs, microservices, and cloud platforms (AWS/Azure/Google).

Responsibilities

• Design and deploy AI-powered applications and automation.
• Develop ML models using Python and AI frameworks.
• Implement RAG pipelines for enterprise apps.
• Collaborate with data scientists, software engineers, and business stakeholders.
• Monitor models and optimize performance and latency.
• Deploy solutions via Docker, Kubernetes, and cloud services.
• Integrate AI models with REST APIs and enterprise apps.
• Develop NLP solutions for text classification and information extraction.

Skills

Python

Machine Learning

NLP

Prompt engineering

Model deployment

Tools

TensorFlow

PyTorch

Scikit-learn

LangChain

LangGraph

LlamaIndex

REST APIs

Docker

Kubernetes

CI/CD

Pinecone

FAISS

ChromaDB

Job description

Quantum Technologies. LLC is seeking an experienced AI Engineer to design, develop, and deploy intelligent applications using AI, ML, and Generative AI.

You will build AI-powered solutions, develop predictive models, integrate large language models, and implement scalable AI systems. You will work with OpenAI, Azure OpenAI, and Hugging Face models, design retrieval-augmented generation pipelines, and optimize performance, latency, and reliability while deploying with Docker, Kubernetes, and