... Retrieval-Augmented Generation (RAG), and Agentic AI models. Key Responsibilities • Design and architect scalable, maintainable AI solutions that integrate Lang chain, LangGraph, and RAG ...
... Retrieval-Augmented Generation (RAG), and Agentic AI models. Key Responsibilities • Design and architect scalable, maintainable AI solutions that integrate Lang chain, LangGraph, and RAG ...
AI Engineer with Python
Sunrise, FL · On-site
Implement Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) * Deliver Gen AI use cases in live production environments Required Skills * Python programming ...
AI Engineer with Python
Sunrise, FL · On-site
Implement Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) * Deliver Gen AI use cases in live production environments Required Skills * Python programming ...
Leverages existing large language models (LLMs), retrieval-augmented generation (RAG) architectures, agentic workflows, data platforms, and software engineering practices to develop scalable ...
Quick apply
Leverages existing large language models (LLMs), retrieval-augmented generation (RAG) architectures, agentic workflows, data platforms, and software engineering practices to develop scalable ...
DOTNET AI Architect
Orlando, FL · On-site
$47 - $52/hr
Understanding of Retrieval-Augmented Generation (RAG) concepts and architectures. * Experience working with unstructured data, document repositories, and content processing solutions. * Knowledge of ...
Quick apply
DOTNET AI Architect
Orlando, FL · On-site
$47 - $52/hr
Understanding of Retrieval-Augmented Generation (RAG) concepts and architectures. * Experience working with unstructured data, document repositories, and content processing solutions. * Knowledge of ...
Engineer IV - Applied AI
Clearwater, FL · On-site
Leverages existing large language models (LLMs), retrieval-augmented generation (RAG) architectures, agentic workflows, data platforms, and software engineering practices to develop scalable ...
Engineer IV - Applied AI
Clearwater, FL · On-site
Leverages existing large language models (LLMs), retrieval-augmented generation (RAG) architectures, agentic workflows, data platforms, and software engineering practices to develop scalable ...
AI/ML Engineer
Miami, FL · On-site +1
$120K - $150K/yr
Develop Retrieval-Augmented Generation (RAG) pipelines. * Work with vector databases for semantic search applications. * Perform data preprocessing, feature engineering, and model evaluation.
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AI/ML Engineer
Miami, FL · On-site +1
$120K - $150K/yr
Develop Retrieval-Augmented Generation (RAG) pipelines. * Work with vector databases for semantic search applications. * Perform data preprocessing, feature engineering, and model evaluation.
Applied AI Field Engineer
Orlando, FL · On-site
Implement retrieval-augmented generation pipelines using enterprise data sources * Build and orchestrate agent-based workflows to automate targeted tasks Model Integration and System Behavior
Applied AI Field Engineer
Orlando, FL · On-site
Implement retrieval-augmented generation pipelines using enterprise data sources * Build and orchestrate agent-based workflows to automate targeted tasks Model Integration and System Behavior
Applied AI Field Engineer
Orlando, FL · On-site
$155K - $190K/yr
Implement retrieval-augmented generation pipelines using enterprise data sources * Build and orchestrate agent-based workflows to automate targeted tasks Model Integration and System Behavior
Applied AI Field Engineer
Orlando, FL · On-site
$155K - $190K/yr
Implement retrieval-augmented generation pipelines using enterprise data sources * Build and orchestrate agent-based workflows to automate targeted tasks Model Integration and System Behavior
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.
Quick apply
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.
Understanding of Retrieval Augmented Generation (RAG) patterns * Experience working with document databases and unstructured data * Exposure to data and analytics solutions * Strong problem-solving ...
New
Understanding of Retrieval Augmented Generation (RAG) patterns * Experience working with document databases and unstructured data * Exposure to data and analytics solutions * Strong problem-solving ...
New
AI Engineer
Lake Mary, FL · On-site
Retrieval-Augmented generation (RAG) * Document Ingestion and preprocessing * Chunking Strategies (semantic, recursive, sliding window) * Embeddings * Vector database - pgvector * Hybrid search ...
Quick apply
AI Engineer
Lake Mary, FL · On-site
Retrieval-Augmented generation (RAG) * Document Ingestion and preprocessing * Chunking Strategies (semantic, recursive, sliding window) * Embeddings * Vector database - pgvector * Hybrid search ...
IT AI Innovation Engineer
Tampa, FL · On-site
Build advanced AI workflows such as retrieval-augmented generation (RAG) pipelines, prompt chaining, contract automation, and interactive Q&A assistants for attorneys and legal support teams. • ...
IT AI Innovation Engineer
Tampa, FL · On-site
Build advanced AI workflows such as retrieval-augmented generation (RAG) pipelines, prompt chaining, contract automation, and interactive Q&A assistants for attorneys and legal support teams. • ...
... retrieval-augmented generation pipelines using enterprise data sources • Build and orchestrate agent-based workflows to automate targeted tasks • Integrate LLM APIs such as Anthropic Claude and ...
... retrieval-augmented generation pipelines using enterprise data sources • Build and orchestrate agent-based workflows to automate targeted tasks • Integrate LLM APIs such as Anthropic Claude and ...
... and retrieval-augmented generation. * Engineering integrations between data platforms, governance, risk, and compliance workflows, and enterprise systems using application programming interfaces ...
... and retrieval-augmented generation. * Engineering integrations between data platforms, governance, risk, and compliance workflows, and enterprise systems using application programming interfaces ...
... and retrieval-augmented generation. * Engineering integrations between data platforms, governance, risk, and compliance workflows, and enterprise systems using application programming interfaces ...
... and retrieval-augmented generation. * Engineering integrations between data platforms, governance, risk, and compliance workflows, and enterprise systems using application programming interfaces ...
... and retrieval-augmented generation. * Engineering integrations between data platforms, governance, risk, and compliance workflows, and enterprise systems using application programming interfaces ...
... and retrieval-augmented generation. * Engineering integrations between data platforms, governance, risk, and compliance workflows, and enterprise systems using application programming interfaces ...
Drive the firm's adoption of AI engineering best practices, including prompt engineering, model evaluation, retrieval-augmented generation (RAG), Model Context Protocol (MCP), vector search, tool ...
Drive the firm's adoption of AI engineering best practices, including prompt engineering, model evaluation, retrieval-augmented generation (RAG), Model Context Protocol (MCP), vector search, tool ...
Drive the firm's adoption of AI engineering best practices, including prompt engineering, model evaluation, retrieval-augmented generation (RAG), Model Context Protocol (MCP), vector search, tool ...
Drive the firm's adoption of AI engineering best practices, including prompt engineering, model evaluation, retrieval-augmented generation (RAG), Model Context Protocol (MCP), vector search, tool ...
Lead delivery of Generative AI solutions utilizing Retrieval-Augmented Generation (RAG) architectures and Large Language Models (LLMs). * Coordinate activities between Government stakeholders ...
Lead delivery of Generative AI solutions utilizing Retrieval-Augmented Generation (RAG) architectures and Large Language Models (LLMs). * Coordinate activities between Government stakeholders ...
ML Engineer
Pembroke Pines, FL · On-site
Responsibilities : • Develop AI-driven reasoning agents and frameworks for automating complex tasks. • Build and optimize RAG (Retrieval-Augmented Generation) pipelines. • Distill and fine-tune ...
ML Engineer
Pembroke Pines, FL · On-site
Responsibilities : • Develop AI-driven reasoning agents and frameworks for automating complex tasks. • Build and optimize RAG (Retrieval-Augmented Generation) pipelines. • Distill and fine-tune ...
Retrieval Augmented Generation information
What does a retrieval augmented generation engineer do?
A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.
What is a retrieval augmented generation?
A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.
What skills and qualifications are needed for retrieval augmented generation?
To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.
What are the most commonly searched types of Retrieval Augmented Generation jobs in Florida? The most popular types of Retrieval Augmented Generation jobs in Florida are:
What are popular job titles related to Retrieval Augmented Generation jobs in Florida? For Retrieval Augmented Generation jobs in Florida, the most frequently searched job titles are:
What job categories do people searching Retrieval Augmented Generation jobs in Florida look for? The top searched job categories for Retrieval Augmented Generation jobs in Florida are:
What cities in Florida are hiring for Retrieval Augmented Generation jobs? Cities in Florida with the most Retrieval Augmented Generation job openings:
Contractor
Re-posted 4 days ago
Job description
**Please strictly adhere to the following resume naming convention:
ALL CAPS, NO SPACES B/T UNDERSCORES
PTN_US_GBAMSREQID_CandidateBeelineID
i.e. PTN_US_9999999_SKIPJOHNSON0413
Bill Rate: market rate/hr
GBaMS ReqID: 10585280
MSP Owner: Tory Robinson
Location: Tampa, FL - 100% Onsite
Duration: 6 + months
AI Lead/Solution Architect
Job Summary
As a Solution Architect with a focus on AI-driven intelligent systems, you will lead the design, development, and
deployment of advanced NLP and cognitive search solutions using Python, Lang chain, LangGraph,
Retrieval-Augmented Generation (RAG), and Agentic AI models.
Key Responsibilities
• Design and architect scalable, maintainable AI solutions that integrate Lang chain, LangGraph, and RAG
methodologies to enhance knowledge discovery and conversational AI capabilities.
• Lead development efforts using Python to prototype and productionize AI agents and workflows.
• Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment
strategies.
• Develop and optimize RAG systems combining vector search, knowledge graphs, and LLMs to provide
contextual and accurate responses.
• Architect agentic AI systems that perform autonomous tasks by chaining actions, managing states, and
integrating external APIs.
• Provide technical leadership and architecture guidance for AI and NLP projects.
• Evaluate emerging AI technologies and frameworks to continuously improve solution design.
• Create comprehensive technical documentation and architecture diagrams to facilitate knowledge transfer.
• Ensure solutions meet security, compliance, and performance standards.
Required Skills and Qualifications
. • Strong proficiency in Python, with experience in backend or AI service development.
• Hands-on experience with Lang chain and/or LangGraph frameworks.
• Deep understanding of Retrieval-Augmented Generation (RAG) systems and techniques.
• Experience designing and implementing agentic AI architectures, autonomous workflows, or
multi-agent systems.
• Familiarity with knowledge graphs, vector search engines (e.g., FAISS, Pinecone, We aviate),
and LLM integration.
• Solid understanding of NLP concepts, transformer models, and prompt engineering.
• Ability to translate complex business requirements into scalable AI solutions.
• Experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker,
• Kubernetes).
• Strong communication skills and ability to collaborate across multidisciplinary teams
Role Descriptions: Job SummaryAs a Solution Architect with a focus on AI-driven intelligent systems| you will lead the design| development| and deployment of advanced NLP and cognitive search solutions using Python| Langchain| LangGraph| Retrieval-Augmented Generation (RAG)| and Agentic AI models.Key ResponsibilitiesDesign and architect scalable| maintainable AI solutions that integrate Langchain| LangGraph| and RAG methodologies to enhance knowledge discovery and conversational AI capabilities.Lead development efforts using Python to prototype and productionize AI agents and workflows.Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment strategies.Develop and optimize RAG systems combining vector search| knowledge graphs| and LLMs to provide contextual and accurate responses.Architect agentic AI systems that perform autonomous tasks by chaining actions| managing states| and integrating external APIs.Provide technical leadership and architecture guidance for AI and NLP projects.Evaluate emerging AI technologies and frameworks to continuously improve solution design.Create comprehensive technical dJob SummaryAs a Solution Architect with a focus on AI-driven intelligent systems| you will lead the design| development| and deployment of advanced NLP and cognitive search solutions using Python| Langchain| LangGraph| Retrieval-Augmented Generation (RAG)| and Agentic AI models.Key ResponsibilitiesDesign and architect scalable| maintainable AI solutions that integrate Langchain| LangGraph| and RAG methodologies to enhance knowledge discovery and conversational AI capabilities.Lead development efforts using Python to prototype and productionize AI agents and workflows.Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment strategies.Develop and optimize RAG systems combining vector search| knowledge graphs| and LLMs to provide contextual and accurate responses.Architect ageocumentation and architecture diagrams to facilitate knowledge transfer.Ensure solutions meet security| compliance| and performance standards..Required Skills and Qualifications. Strong proficiency in Python| with experience in backend or AI service development.Hands-on experience with Langchain andor LangGraph frameworks.Deep understanding of Retrieval-Augmented Generation (RAG) systems and techniques.Experience designing and implementing agentic AI architectures| autonomous workflows| or multi-agent systems.Familiarity with knowledge graphs| vector search engines (e.g.| FAISS| Pinecone| Weaviate)| and LLM integration.Solid understanding of NLP concepts| transformer models| and prompt engineering.Ability to translate complex business requirements into scalable AI solutions.Experience with cloud platforms (AWS| GCP| Azure) and container orchestration (Docker| Kubernetes).Strong communication skills and ability to collaborate across multidisciplinary teams
Essential Skills: Job SummaryAs a Solution Architect with a focus on AI-driven intelligent systems| you will lead the design| development| and deployment of advanced NLP and cognitive search solutions using Python| Langchain| LangGraph| Retrieval-Augmented Generation (RAG)| and Agentic AI models.Key ResponsibilitiesDesign and architect scalable| maintainable AI solutions that integrate Langchain| LangGraph| and RAG methodologies to enhance knowledge discovery and conversational AI capabilities.Lead development efforts using Python to prototype and productionize AI agents and workflows.Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment strategies.Develop and optimize RAG systems combining vector search| knowledge graphs| and LLMs to provide contextual and accurate responses.Architect agentic AI systems that perform autonomous tasks by chaining actions| managing states| and integrating external APIs.Provide technical leadership and architecture guidance for AI and NLP projects.Ev
ALL CAPS, NO SPACES B/T UNDERSCORES
PTN_US_GBAMSREQID_CandidateBeelineID
i.e. PTN_US_9999999_SKIPJOHNSON0413
Bill Rate: market rate/hr
GBaMS ReqID: 10585280
MSP Owner: Tory Robinson
Location: Tampa, FL - 100% Onsite
Duration: 6 + months
AI Lead/Solution Architect
Job Summary
As a Solution Architect with a focus on AI-driven intelligent systems, you will lead the design, development, and
deployment of advanced NLP and cognitive search solutions using Python, Lang chain, LangGraph,
Retrieval-Augmented Generation (RAG), and Agentic AI models.
Key Responsibilities
• Design and architect scalable, maintainable AI solutions that integrate Lang chain, LangGraph, and RAG
methodologies to enhance knowledge discovery and conversational AI capabilities.
• Lead development efforts using Python to prototype and productionize AI agents and workflows.
• Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment
strategies.
• Develop and optimize RAG systems combining vector search, knowledge graphs, and LLMs to provide
contextual and accurate responses.
• Architect agentic AI systems that perform autonomous tasks by chaining actions, managing states, and
integrating external APIs.
• Provide technical leadership and architecture guidance for AI and NLP projects.
• Evaluate emerging AI technologies and frameworks to continuously improve solution design.
• Create comprehensive technical documentation and architecture diagrams to facilitate knowledge transfer.
• Ensure solutions meet security, compliance, and performance standards.
Required Skills and Qualifications
. • Strong proficiency in Python, with experience in backend or AI service development.
• Hands-on experience with Lang chain and/or LangGraph frameworks.
• Deep understanding of Retrieval-Augmented Generation (RAG) systems and techniques.
• Experience designing and implementing agentic AI architectures, autonomous workflows, or
multi-agent systems.
• Familiarity with knowledge graphs, vector search engines (e.g., FAISS, Pinecone, We aviate),
and LLM integration.
• Solid understanding of NLP concepts, transformer models, and prompt engineering.
• Ability to translate complex business requirements into scalable AI solutions.
• Experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker,
• Kubernetes).
• Strong communication skills and ability to collaborate across multidisciplinary teams
Role Descriptions: Job SummaryAs a Solution Architect with a focus on AI-driven intelligent systems| you will lead the design| development| and deployment of advanced NLP and cognitive search solutions using Python| Langchain| LangGraph| Retrieval-Augmented Generation (RAG)| and Agentic AI models.Key ResponsibilitiesDesign and architect scalable| maintainable AI solutions that integrate Langchain| LangGraph| and RAG methodologies to enhance knowledge discovery and conversational AI capabilities.Lead development efforts using Python to prototype and productionize AI agents and workflows.Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment strategies.Develop and optimize RAG systems combining vector search| knowledge graphs| and LLMs to provide contextual and accurate responses.Architect agentic AI systems that perform autonomous tasks by chaining actions| managing states| and integrating external APIs.Provide technical leadership and architecture guidance for AI and NLP projects.Evaluate emerging AI technologies and frameworks to continuously improve solution design.Create comprehensive technical dJob SummaryAs a Solution Architect with a focus on AI-driven intelligent systems| you will lead the design| development| and deployment of advanced NLP and cognitive search solutions using Python| Langchain| LangGraph| Retrieval-Augmented Generation (RAG)| and Agentic AI models.Key ResponsibilitiesDesign and architect scalable| maintainable AI solutions that integrate Langchain| LangGraph| and RAG methodologies to enhance knowledge discovery and conversational AI capabilities.Lead development efforts using Python to prototype and productionize AI agents and workflows.Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment strategies.Develop and optimize RAG systems combining vector search| knowledge graphs| and LLMs to provide contextual and accurate responses.Architect ageocumentation and architecture diagrams to facilitate knowledge transfer.Ensure solutions meet security| compliance| and performance standards..Required Skills and Qualifications. Strong proficiency in Python| with experience in backend or AI service development.Hands-on experience with Langchain andor LangGraph frameworks.Deep understanding of Retrieval-Augmented Generation (RAG) systems and techniques.Experience designing and implementing agentic AI architectures| autonomous workflows| or multi-agent systems.Familiarity with knowledge graphs| vector search engines (e.g.| FAISS| Pinecone| Weaviate)| and LLM integration.Solid understanding of NLP concepts| transformer models| and prompt engineering.Ability to translate complex business requirements into scalable AI solutions.Experience with cloud platforms (AWS| GCP| Azure) and container orchestration (Docker| Kubernetes).Strong communication skills and ability to collaborate across multidisciplinary teams
Essential Skills: Job SummaryAs a Solution Architect with a focus on AI-driven intelligent systems| you will lead the design| development| and deployment of advanced NLP and cognitive search solutions using Python| Langchain| LangGraph| Retrieval-Augmented Generation (RAG)| and Agentic AI models.Key ResponsibilitiesDesign and architect scalable| maintainable AI solutions that integrate Langchain| LangGraph| and RAG methodologies to enhance knowledge discovery and conversational AI capabilities.Lead development efforts using Python to prototype and productionize AI agents and workflows.Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment strategies.Develop and optimize RAG systems combining vector search| knowledge graphs| and LLMs to provide contextual and accurate responses.Architect agentic AI systems that perform autonomous tasks by chaining actions| managing states| and integrating external APIs.Provide technical leadership and architecture guidance for AI and NLP projects.Ev