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Ai Rag Jobs in Florida (NOW HIRING)

We are expanding our AI/ML capabilities to include generative AI-driven solutions, RAG applications, and predictive models for retail pricing using collected data from multiple sources. We are ...

We are expanding our AI/ML capabilities to include generative AI-driven solutions, RAG applications, and predictive models for retail pricing using collected data from multiple sources. We are ...

Lead AI/ML Engineer

Tampa, FL · On-site

$96.90K - $127.60K/yr

Architect and develop Generative AI applications using RAG frameworks for enterprise-scale solutions. * Design and implement robust system architectures for AI-driven platforms ensuring scalability ...

The role focuses on LLM-based architectures, agentic workflows, RAG pipelines, and end-to-end model ... Design and implement AI/ML solutions from POC to production * Translate business requirements into ...

Design and operate RAG systems and AI search capabilities (Azure AI Search, hybrid search, semantic ranking) that surface relevant content while inheriting source system permissions * Develop ...

Key Responsibilities Architect and develop Generative AI applications using RAG frameworks for enterprise-scale solutions. Design and implement robust system architectures for AI-driven platforms ...

Design and operate RAG systems and AI search capabilities (Azure AI Search, hybrid search, semantic ranking) that surface relevant content while inheriting source system permissions * Develop ...

Senior Agentic (AI) Engineer

Orlando, FL · On-site +1

$97.60K - $134K/yr

Worth AI is hiring a Senior Agentic AI Engineer to design and ship production agent systems that ... Strong RAG fundamentals chunking, embeddings, hybrid retrieval, reranking, grounding - and judgment ...

Senior Agentic (AI) Engineer

Orlando, FL · On-site +1

$97.60K - $134K/yr

Worth AI is hiring a Senior Agentic AI Engineer to design and ship production agent systems that ... Strong RAG fundamentals chunking, embeddings, hybrid retrieval, reranking, grounding - and judgment ...

Sr. Gen AI Developer

Tampa, FL · On-site

$114.80K - $154.50K/yr

Proven experience designing and delivering LLM / GenAI solutions (e.g., RAG, orchestration, prompt engineering, AI-assisted automation). Solid understanding of microservices, APIs, distributed ...

Senior Agentic (AI) Engineer

Miami, FL · On-site +1

$99.90K - $137.20K/yr

Worth AI is hiring a Senior Agentic AI Engineer to design and ship production agent systems that ... Strong RAG fundamentals chunking, embeddings, hybrid retrieval, reranking, grounding - and judgment ...

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Ai Rag information

What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

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 some common challenges faced by AI RAG (Retrieval-Augmented Generation) 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 are AI RAGs?

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 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 cities in Florida are hiring for Ai Rag jobs? Cities in Florida with the most Ai Rag job openings:

AI/ML Engineer

ReturnPro

Aventura, FL • On-site

Full-time

Posted 14 days ago


Job description

We are a leading provider of reverse logistics and returns management solutions, leveraging technology to optimize supply chains and maximize value recovery. We are expanding our AI/ML capabilities to include generative AI-driven solutions, RAG applications, and predictive models for retail pricing using collected data from multiple sources.
We are seeking an AI/ML Engineer with expertise in generative AI, RAG applications, AI agentic frameworks, and predictive modeling. This role will focus on developing pricing models for retail products, enhancing operational efficiency through AI automation, and applying cutting-edge techniques in LLMs, NLP, and agentic AI frameworks.
Primary Responsibilities/Essential Functions
This job description in no way states or implies that these are the only duties to be performed by the teammate occupying this position. The selected candidate may perform other related duties assigned to meet the ongoing needs of the business.
Key Responsibilities
  • Design, build, and deploy predictive models for retail pricing using data from various internal and external sources.
  • Develop and fine-tune generative AI models (LLMs) for automation, data augmentation, and content generation.
  • Implement RAG (Retrieval-Augmented Generation) applications to enhance AI systems with dynamic information retrieval.
  • Build and integrate AI agentic frameworks for autonomous decision-making and task automation.
  • Build and maintain scalable machine learning pipelines for data processing, training, and inference.
  • Collaborate with cross-functional teams (data engineering, operations, and business) to define AI/ML use cases and deliver solutions.
  • Monitor and improve model performance, ensuring robustness, scalability, and reliability.
  • Utilize tools like OpenAI API, Hugging Face, LangChain, LlamaIndex, and cloud platforms (AWS, Azure, GCP) for AI development and deployment.

Required:
• 5+ years of experience in AI/ML engineering with a strong focus on generative AI, RAG applications, and predictive modeling.
• Proficiency in Python and AI/ML libraries like TensorFlow, PyTorch, and Scikit-Learn.
• Hands-on experience with LLMs, NLP models, prompt engineering, and tools like OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex, and AI agentic frameworks.
• Strong understanding of data preprocessing, feature engineering, and model selection for time series and pricing data.
• Experience in building and deploying ML models on cloud platforms (AWS SageMaker, GCP Vertex AI, or Azure ML).
• Knowledge of MLOps best practices, including CI/CD pipelines, version control, and model monitoring.
• Excellent problem-solving skills and ability to communicate complex AI concepts clearly.
Preferred:
• Experience with AI-driven pricing optimization in retail, logistics, or e-commerce.
• Experience developing and deploying RAG systems for dynamic content retrieval.
• Familiarity with AI agentic frameworks for building autonomous AI agents.
• Prior work in AI automation for supply chain, demand forecasting, or pricing strategies.
• Strong knowledge of AI/ML ethics, ensuring fairness and bias mitigation in models.