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Summer Retrieval Augmented Generation Jobs (NOW HIRING)

XPath Solutions

Generative AI Engineer

Charlotte, NC · On-site

$60 - $72/hr

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 ...

ARA

Senior AI Technologist

Raleigh, NC · On-site

$48.75 - $63/hr

Working closely with business units, engineers, and functional teams, you will leverage applied AI technologies including large language models (LLMs), retrieval-augmented generation (RAG), AI agents ...

Merican

Full Stack AI Engineer (Generative AI)

Design and implement Retrieval-Augmented Generation (RAG) architectures using enterprise data sources. * Integrate AI capabilities into existing Java, .NET, or Node.js enterprise applications.

Closure Technologies

AI/ML Engineer

Mclean, VA

Closure Technologies is seeking a AI/ML Engineer who will Implement and maintain Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) into applications, supported ...

Cliff Services Inc

Qdrant Developer

Alpharetta, GA · On-site

Knowledge of RAG (Retrieval-Augmented Generation) architectures. * Experience integrating Qdrant with LLM frameworks such as LangChain or LlamaIndex. * Familiarity with REST APIs and microservices.

ClifyX

AI Architect

Plano, TX · On-site

GPT, Claude • Prompt Engineering • RAG (Retrieval Augmented Generation) • AWS Cloud • Strong architectural and hands on GenAI expertise • Experience with enterprise automation and testing ...

Accord Technologies Inc.

GEN AI Architect

New York, NY · On-site

The ideal candidate will have a strong background in investment banking, hands-on experience with Microsoft Azure OpenAI, and expertise in Retrieval-Augmented Generation (RAG). Key Responsibilities:

Connexions Data Inc

Gen AI Developer

Seattle, WA · On-site

$57.25 - $78.75/hr

Proficiency in RAG (Retrieval-Augmented Generation) techniques. Strong understanding of natural language processing (NLP). Experience with data preprocessing and model fine-tuning. Familiarity with ...

CYNET SYSTEMS

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$70 - $75/hr

This role involves applying large language models, retrieval-augmented generation, multi-agent orchestration, and foundation model capabilities to automate and enhance privacy operations. Requirement ...

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All JobsSummer Retrieval Augmented Generation Jobs

Summer Retrieval Augmented Generation information

What are the key skills and qualifications needed to thrive as a Retrieval Augmented Generation (RAG) Engineer, and why are they important?

To thrive as a Retrieval Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and information retrieval, typically supported by a degree in computer science or a related field. Proficiency with frameworks like PyTorch or TensorFlow, experience with vector databases (e.g., FAISS, Pinecone), and familiarity with LLM APIs are commonly required. Creative problem-solving, strong communication, and the ability to collaborate across multidisciplinary teams are essential soft skills. These competencies ensure effective development, deployment, and optimization of advanced AI systems that integrate retrieval and generative capabilities.

What is a Summer Retrieval Augmented Generation role?

A Summer Retrieval Augmented Generation (RAG) role typically refers to a summer position focused on developing or improving retrieval-augmented generation systems, which are AI models that combine information retrieval with generative capabilities. In this role, you might work on integrating search algorithms with large language models, enabling systems to fetch relevant information from external sources and generate accurate, context-aware responses. These positions are often found in research labs, tech companies, or startups working on advanced AI applications, and are ideal for students or early-career professionals interested in machine learning, natural language processing, and AI research.

What are some common challenges faced when working on Retrieval-Augmented Generation (RAG) projects during a summer internship?

During a summer internship focused on Retrieval-Augmented Generation (RAG), interns often encounter challenges such as integrating retrieval systems with generative models, managing large-scale datasets, and optimizing latency for real-time responses. Collaboration with cross-functional teams—including data engineers, research scientists, and product managers—is essential for aligning project goals and troubleshooting implementation issues. Additionally, interns may need to balance exploratory research with delivering usable prototypes within tight timeframes, which helps develop both technical and project management skills.
What cities are hiring for Summer Retrieval Augmented Generation jobs? Cities with the most Summer Retrieval Augmented Generation job openings:
What are the most commonly searched types of Retrieval Augmented Generation jobs? The most popular types of Retrieval Augmented Generation jobs are:
What states have the most Summer Retrieval Augmented Generation jobs? States with the most job openings for Summer Retrieval Augmented Generation jobs include:
Summer Retrieval Augmented Generation jobs near you

Generative AI Engineer

XPath Solutions

Charlotte, NC • On-site

$60 - $72/hr

Full-time

Posted 9 days ago

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