1

Ai Rag Jobs in Delray Beach, FL (NOW HIRING)

Applied AI Engineer

Sunrise, FL · On-site

$50 - $55/hr

Experience with LangGraph, RAG, or MCP style integrations. * Experience with generative AI and prompt engineering. * Experience in continuous integration/continuous deployment pipelines and ...

Seez is an AI-powered automotive technology company transforming how vehicles are bought and sold ... Build and optimise Retrieval-Augmented Generation (RAG) pipelines and vector search solutions.

New

Experience with LangGraph, RAG, or MCP-style integrations * Hands-on experience with generative AI and prompt engineering * Experience with CI/CD pipelines and containerized deployments Preferred ...

Develop and optimize Retrieval-Augmented Generation (RAG) systems, including embeddings, vector search, retrieval pipelines, chunking strategies, and relevance tuning. * Build multimodal AI workflows ...

Does not act as the primary technical owner for RAG frameworks, vector databases, or embedding ... AI solutions benefit from more relevant, uptodate, and understandable data . * Clear ownership and ...

AI Engineer

Sunrise, FL · On-site

$100K - $130K/yr

Develop advanced Generative AI applications leveraging Large Language Models (LLMs), RAG architectures, and AI agents. Design and implement Agentic AI frameworks using LangGraph, LangChain, and ...

As an AI Engineer , you will be the technical engine behind every AI implementation the company ... Semantic similarity and coherence metrics for RAG-based applications * Golden dataset management ...

next page

Showing results 1-20

Ai Rag information

See Delray Beach, FL salary details

$30K

$54.7K

$78.4K

How much do ai rag jobs pay per year?

As of Aug 20, 2026, the average yearly pay for ai rag in Delray Beach, FL is $54,693.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,000.00 and $61,000.00 per year, depending on experience, location, and employer.

What is an AI RAG?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

What are the key skills and qualifications needed to thrive as an AI researcher?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What are common challenges faced by AI RAG engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

What job categories do people searching Ai Rag jobs in Delray Beach, FL look for?

The top searched job categories for Ai Rag jobs in Delray Beach, FL are:

What cities near Delray Beach, FL are hiring for Ai Rag jobs?

Cities near Delray Beach, FL with the most Ai Rag job openings:

Infographic showing various Ai Rag job openings in Delray Beach, FL as of August 2026, with employment types broken down into 71% Full Time, 26% Part Time, and 3% Contract. Highlights an 65% Physical, 4% Hybrid, and 31% Remote job distribution, with an average salary of $54,693 per year, or $26.3 per hour.

Gen AI Engineer - Sunrise, FL - Contract Opportunity

Zodiac Solutions

Sunrise, FL • On-site

Contractor

Re-posted 27 days ago


Job description

Role: Gen AI Engineer

Location: Sunrise, FL (Onsite/Hybrid as per client requirement)
Duration: Long-Term Contract
Required Skills: Python, GenAI, LLM, LangChain, LangGraph, RAG.


Experience: 6+ Years

Job Description:

  • Design and develop AI-powered applications using Python and modern GenAI frameworks.
  • Build and optimize LLM-based solutions using LangChain and LangGraph.
  • Develop RAG pipelines by integrating vector databases and enterprise knowledge sources.
  • Experience with prompt engineering, RAG (Retrieval-Augmented Generation), and vector databases.
  • Knowledge of AI model integration using APIs such as OpenAI, Anthropic, or similar platforms.
  • Experience developing and deploying AI applications in cloud environments (AWS, Azure, or GCP).
  • Strong understanding of REST APIs, microservices, and scalable application architecture.
  • Familiarity with Git, CI/CD pipelines, and Agile methodologies.