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Freelance Retrieval Augmented Generation Jobs in Oklahoma

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 ...

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 ...

Senior Data/AI Engineer

Oklahoma City, OK · Remote

$98.50K - $133.80K/yr

... NLU, retrieval-augmented generation (RAG), document-level LLM extraction, and agentic frameworks applied to EHR/EMR, practice management (PM), pharmacy, claims, and clinical note data.

Data & AI Engineer

Bartlesville, OK · On-site

$98.30K - $118K/yr

Experience with LLM tools and frameworks such as LangChain, OpenAI APIs, Retrieval-Augmented Generation (RAG) . * Familiarity with vector stores (e.g., FAISS, Weaviate) and embedding-based search.

Data & AI Engineer

Bartlesville, OK · On-site

$98.30K - $118K/yr

Experience with LLM tools and frameworks such as LangChain, OpenAI APIs, Retrieval-Augmented Generation (RAG) . * Familiarity with vector stores (e.g., FAISS, Weaviate) and embedding-based search.

Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, ensuring high-fidelity context retrieval and vector database management * Implement and extend MCP (Model Context Protocol ...

Data & AI Engineer

Bartlesville, OK · On-site

$98.30K - $118K/yr

Experience with LLM tools and frameworks such as LangChain, OpenAI APIs, Retrieval-Augmented Generation (RAG) . * Familiarity with vector stores (e.g., FAISS, Weaviate) and embedding-based search.

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Freelance Retrieval Augmented Generation information

What are the key skills and qualifications needed to thrive as a Freelance Retrieval Augmented Generation Specialist, and why are they important?

To thrive as a Freelance Retrieval Augmented Generation (RAG) Specialist, you need expertise in natural language processing, information retrieval, and machine learning, typically supported by a degree in computer science or related fields. Proficiency with frameworks like Hugging Face Transformers, vector databases (e.g., FAISS, Pinecone), and cloud platforms is often required. Strong problem-solving, effective communication, and adaptability set standout professionals apart in this role. These skills ensure the development and fine-tuning of high-performance RAG systems that deliver accurate, contextually relevant results for clients.

How do Freelance Retrieval Augmented Generation specialists typically collaborate with client teams during a project?

Freelance Retrieval Augmented Generation (RAG) specialists often work closely with client data scientists, engineers, and project managers to understand business requirements and integrate RAG systems into existing workflows. Communication is usually handled through regular virtual meetings, shared documentation, and sometimes real-time collaboration tools. Freelancers are expected to deliver modular, well-documented solutions and provide guidance on optimizing retrieval pipelines or fine-tuning models. This collaborative dynamic ensures that RAG implementations are aligned with client goals and technical standards, while also allowing freelancers to contribute innovative solutions based on their expertise.

What is a Freelance Retrieval Augmented Generation (RAG) specialist?

A Freelance Retrieval Augmented Generation (RAG) specialist is an independent professional who designs, develops, and implements AI systems that combine retrieval-based methods with generative models. RAG specialists help organizations enhance their applications by integrating large language models (LLMs) with external data sources, allowing the AI to access and utilize up-to-date information beyond its training data. Their work involves tasks such as building pipelines for document indexing and retrieval, fine-tuning models, and optimizing the integration for accuracy and efficiency. Freelance RAG specialists typically work on a contract basis, offering flexibility and expertise for businesses that need advanced AI solutions.
What are the most commonly searched types of Retrieval Augmented Generation jobs in Oklahoma? The most popular types of Retrieval Augmented Generation jobs in Oklahoma are:
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Applied & Agentic AI Engineer

Applied & Agentic AI Engineer

Sedgwick

Tulsa, OK • On-site

Other

This job post has expired today. Applications are no longer accepted.


Sedgwick rating

7.5

Company rating: 7.5 out of 10

Based on 305 frontline employees who took The Breakroom Quiz

195th of 258 rated insurance


Job description

By joining Sedgwick, you'll be part of something truly meaningful. It's what our 33,000 colleagues do every day for people around the world who are facing the unexpected. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there's no limit to what you can achieve.

Newsweek Recognizes Sedgwick as America's Greatest Workplaces National Top Companies

Certified as a Great Place to Work®

Fortune Best Workplaces in Financial Services & Insurance

Applied & Agentic AI Engineer

Job Responsibilities

  • Architect and deploy LLM-powered and agentic AI solutions that transform claims intake, policy interpretation, fraud detection, and resolution workflows.

  • 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 capable of reasoning, planning, and executing multi-step claims processes.

  • Develop stateful workflow orchestration layers that manage context, memory, and task sequencing across interactions.

  • Implement planning and reflection loops that decompose complex claims scenarios into structured subtasks.

  • Enable dynamic tool use through function calling and secure API integrations with claims systems, CRM platforms, document repositories, and analytics tools.

  • Develop document intelligence pipelines using LLMs for summarization, entity extraction, classification, validation, and timeline reconstruction.

  • Design structured prompt frameworks that enforce deterministic outputs and domain-aware reasoning.

  • Build multi-agent systems that coordinate document review, coverage analysis, compliance checks, and decision support.

  • Implement human-in-the-loop checkpoints for escalation, review, and override of AI-driven decisions.

  • Develop guardrails, output validation layers, and hallucination mitigation strategies.

  • Enforce structured outputs using schemas, type validation, and deterministic post-processing logic.

  • Optimize token consumption, inference latency, and cloud infrastructure costs.

  • Deploy scalable AI microservices using containerization and cloud-native architectures.

  • Implement monitoring for model drift, retrieval quality degradation, reasoning failures, and workflow breakdowns.

  • Maintain detailed audit logs of model decisions, agent reasoning steps, and tool executions.

  • Develop evaluation frameworks to test reasoning accuracy, workflow completion rates, and system reliability.

  • Collaborate with data engineering to build embedding pipelines, feature stores, and vector indexing strategies.

  • Ensure compliance with Responsible AI standards, data privacy regulations, and enterprise governance policies.

  • Partner with claims operations leadership to embed AI capabilities directly into adjuster and supervisor workflows.

  • Measure business impact through cycle-time reduction, automation coverage, fraud detection lift, and operational efficiency gains.

Qualifications

  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, or related field.

  • 5+ years of experience building production-grade AI or advanced software systems.

  • 2-4+ years of hands-on experience with LLM-powered applications and orchestration layers.

  • Strong expertise in retrieval-augmented generation architectures and vector search systems.

  • Experience designing and implementing multi-agent systems and workflow orchestration engines.

  • Deep understanding of planning loops, contextual memory, and tool-augmented LLM reasoning.

  • Strong proficiency in Python and API-driven system design.

  • Experience integrating enterprise platforms and building secure connectors.

  • Familiarity with Azure OpenAI or similar enterprise LLM environments.

  • Experience deploying containerized services and managing CI/CD pipelines.

  • Understanding of distributed systems, microservices, and event-driven architectures.

  • Experience implementing guardrails, access controls, and auditability mechanisms.

  • Strong knowledge of evaluation methodologies for LLM reliability and agent performance.

  • Experience in insurance, claims, healthcare, or other regulated industries preferred.

  • Ability to translate complex operational workflows into scalable, AI-driven autonomous systems.

Sedgwick is an Equal Opportunity Employer and a Drug-Free Workplace.

If you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, consider applying for it anyway! Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience. You may be just the right candidate for this or other roles.

Sedgwick is the world's leading risk and claims administration partner, which helps clients thrive by navigating the unexpected. The company's expertise, combined with the most advanced AI-enabled technology available, sets the standard for solutions in claims administration, loss adjusting, benefits administration, and product recall. With over 33,000 colleagues and 10,000 clients across 80 countries, Sedgwick provides unmatched perspective, caring that counts, and solutions for the rapidly changing and complex risk landscape. For more, see sedgwick.com


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