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Retrieval Augmented Generation Jobs in Kansas (NOW HIRING)

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 ...

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal drafting * Curate and maintain a high-quality answer corpus: writing net-new content, retiring stale ...

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal drafting * Curate and maintain a high-quality answer corpus: writing net-new content, retiring stale ...

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal drafting * Curate and maintain a high-quality answer corpus: writing net-new content, retiring stale ...

AI Architect

Wichita, KS · On-site

$56.25 - $74.25/hr

... retrieval-augmented generation (RAG), embeddings, and vector stores • Proven experience communicating AI and technology concepts in both execution detail and broad terms to a variety of technical ...

New

... for retrieval-augmented generation (RAG), multi-agent orchestration, and autonomous workflow automation. · Design and implement advanced agentic systems capable of planning, reasoning, tool ...

Lead AI Engineer

Kansas City, KS · On-site

$98K - $130K/yr

Familiarity with LangChain, LangGraph, vector databases, and Retrieval-Augmented Generation (RAG) pipelines. DevOps & Observability: Strong understanding of CI/CD pipelines, monitoring, logging, and ...

Data & AI Engineer

Lawrence, KS

$108K - $129K/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

Kansas City, KS

$104K - $125K/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

Lawrence, KS

$104K - $125K/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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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 Kansas? The most popular types of Retrieval Augmented Generation jobs in Kansas are:
What are popular job titles related to Retrieval Augmented Generation jobs in Kansas? For Retrieval Augmented Generation jobs in Kansas, the most frequently searched job titles are:
What job categories do people searching Retrieval Augmented Generation jobs in Kansas look for? The top searched job categories for Retrieval Augmented Generation jobs in Kansas are:
What cities in Kansas are hiring for Retrieval Augmented Generation jobs? Cities in Kansas with the most Retrieval Augmented Generation job openings:
Infographic showing various Retrieval Augmented Generation job openings in Kansas as of June 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 75% In-person, and 25% Remote job distribution.
Applied & Agentic AI Engineer

Applied & Agentic AI Engineer

Sedgwick

Wichita, KS

Other

Posted 6 days ago


Sedgwick rating

7.5

Company rating: 7.5 out of 10

Based on 308 frontline employees who took The Breakroom Quiz

187th of 261 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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