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Summer Retrieval Augmented Generation Jobs (NOW HIRING)

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:

Additionally, experience in building Retrieval-Augmented Generation (RAG) pipelines for search and chat applications is highly desired. Key Responsibilities: * Develop and optimize NLP models for ...

Senior AI Engineer

Hartford, CT · On-site

$55.75 - $71.75/hr

Mandatory Skills Google CCAI (Contact Center AI) Google Vertex AI RAG (Retrieval Augmented Generation) AI/ML Google GCP MLOps Healthcare Payer Cloud AI Platform Summary As an AI engineer, you will be ...

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

Job Summary : Closure Technologies is seeking an AI/ML Engineer who will implement and maintain Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) into ...

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:

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search technologies * Create, test, and refine prompts, structured outputs, and evaluation ...

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search technologies * Create, test, and refine prompts, structured outputs, and evaluation ...

Knowledge of LLMs, AI agents, and Retrieval-Augmented Generation (RAG) frameworks. Responsibilities: * Design, develop, and maintain scalable microservices using Core Java and Spring Boot. * Build ...

Integrate with large language models (LLMs) and generative AI (GenAI) using prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) techniques. * Implement MCP client and server ...

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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:
Python Software Developer (Python AWS, RAG) / on W2

Python Software Developer (Python AWS, RAG) / on W2

Noblesoft Solutions Inc.

Saint Petersburg, FL • Hybrid

$47.50 - $65.50/hr

Other

Posted 11 days ago


Job description

Position: Python Software Developer (Python AWS, RAG)
Contract Duration: Long Term
Location: St. Petersburg, FL (Hybrid, 3 days onsite)
Only Locals candidates

Job Description
We are seeking a talented and passionate Python Developer to join our team and contribute to the development and implementation of innovative Retrieval-Augmented Generation (RAG) systems. You will play a crucial role in designing, building, and maintaining applications that leverage the power of large language models (LLMs) and advanced retrieval techniques to create cutting-edge AI solutions.

Skills:

Strong Python programming skills:
Demonstrated proficiency in Python programming, including experience with relevant libraries and frameworks (e.g., FastAPI, Flask, Pandas, NumPy).
Experience with LLMs and RAG systems:
Familiarity with large language models and retrieval-augmented generation techniques, including experience with LLM APIs and retrieval systems.

Experience with data retrieval and indexing:
Experience with data retrieval from various sources (e.g., databases, APIs, file systems) and building and managing retrieval indices.

Knowledge of data structures and algorithms:
Understanding of fundamental data structures and algorithms relevant to building efficient and scalable RAG applications.

Experience with cloud computing platforms (e.g., AWS, Google Cloud Platform, Azure):
Familiarity with cloud computing platforms and their services for deploying and scaling RAG applications.

Strong problem-solving and analytical skills:
Ability to identify, analyze, and solve complex problems related to data retrieval, LLM integration, and RAG system optimization.

Bonus Points:
Experience with specific LLM frameworks (e.g., LangChain, Hugging Face Transformers).
Familiarity with search engines and information retrieval techniques.
Experience with machine learning and deep learning concepts.
Experience with building and deploying production-ready applications.
Contribution to open-source projects related to RAG or LLMs.

Education:
Bachelor''s degree in Computer Science, Software Engineering, or related technical field required
Master''s degree in Computer Science, AI/ML, or related field preferred
Relevant professional certifications in Python, cloud platforms, or AI/ML technologies are a plus