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Retrieval Augmented Generation Jobs in Florida (NOW HIRING)

Tata Consultancy Service Limited

AI Developer

Tampa, FL · On-site

$100K - $130K/yr

Neural Networks, Decision Trees, SVM, NLP, Reinforcement Learning, Ensemble Methods, MCP • Strong knowledge with RAG (Retrieval-Augmented Generation), Prompt Engineering, Agentic AI • Knowledge ...

Advantive

AI Software Developer

Tampa, FL · On-site

You will work across LLM integrations, retrieval-augmented generation (RAG) systems, and AI-driven workflows, building reliable backend services and applications that directly impact enterprise ...

Tror AI for everyone

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 ...

Incedo Inc.

Frontend AI Engineer

Tampa, FL · On-site +1

Implement Retrieval-Augmented Generation (RAG) frameworks for enterprise knowledge solutio * ns.Optimize application performance, scalability, and user experien * ce.Collaborate with product managers ...

New

Advantive

AI Software Developer

Tampa, FL · On-site

You will work across LLM integrations, retrieval-augmented generation (RAG) systems, and AI-driven workflows, building reliable backend services and applications that directly impact enterprise ...

Sedgwick

Applied & Agentic AI Engineer

Tampa, FL

Design end-to-end retrieval-augmented generation (RAG) systems leveraging enterprise knowledge bases, policy documents, SOPs, and historical claims data. * Build autonomous and semi-autonomous agents ...

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Retrieval Augmented Generation information

What are the typical daily responsibilities of a Retrieval Augmented Generation engineer?

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 job?

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 are the key skills and qualifications needed to thrive in the Retrieval Augmented Generation position, and why are they important?

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:
Retrieval Augmented Generation jobs near you
Infographic showing various Retrieval Augmented Generation job openings in Florida as of June 2026, with employment types broken down into 77% Full Time, and 23% Contract. Highlights an 80% In-person, and 20% Remote job distribution.

Information Technology_USA - USA_Developer

SysMind Tech

Tampa, FL • On-site

Contractor

Posted 13 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

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