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 ...
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 ...
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 ...
Own end-to-end retrieval-augmented generation (RAG) implementations (ingestion, chunking, embedding, indexing, retrieval, orchestration); define prompt engineering standards and evaluation harnesses ...
Own end-to-end retrieval-augmented generation (RAG) implementations (ingestion, chunking, embedding, indexing, retrieval, orchestration); define prompt engineering standards and evaluation harnesses ...
RAG (Retrieval-Augmented Generation) * Prompt engineering * Vector databases (design/usage/integration) * Model build + deployment * GenAI model build: training, fine-tuning, validation * Model ...
RAG (Retrieval-Augmented Generation) * Prompt engineering * Vector databases (design/usage/integration) * Model build + deployment * GenAI model build: training, fine-tuning, validation * Model ...
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 ...
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 ...
Optimizestructured data for RAG (Retrieval-Augmented Generation) environments. * Collaborate with GEO and content teams to ensure alignment between semantic layer and markup layer. * Reduce friction ...
Optimizestructured data for RAG (Retrieval-Augmented Generation) environments. * Collaborate with GEO and content teams to ensure alignment between semantic layer and markup layer. * Reduce friction ...
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
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
Familiar with RAG (Retrieval-Augmented Generation) pipelines, vector databases, or orchestration frameworks. * Capable of working with GraphQL, microservices, or event-driven systems in fast-paced ...
Familiar with RAG (Retrieval-Augmented Generation) pipelines, vector databases, or orchestration frameworks. * Capable of working with GraphQL, microservices, or event-driven systems in fast-paced ...
Senior Applied & Agentic AI Engineer
$98K - $135K/yr
... for retrieval-augmented generation (RAG), multi-agent orchestration, and autonomous workflow automation. · Design and implement advanced agentic systems capable of planning, reasoning, tool ...
Senior Applied & Agentic AI Engineer
$98K - $135K/yr
... for retrieval-augmented generation (RAG), multi-agent orchestration, and autonomous workflow automation. · Design and implement advanced agentic systems capable of planning, reasoning, tool ...
Senior Applied & Agentic AI Engineer
$103K - $141K/yr
... for retrieval-augmented generation (RAG), multi-agent orchestration, and autonomous workflow automation. · Design and implement advanced agentic systems capable of planning, reasoning, tool ...
Senior Applied & Agentic AI Engineer
$103K - $141K/yr
... 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 ...
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 ...
Senior Data Scientist
Olathe, KS · On-site
Lead the design and implementation of end-to-end Retrieval-Augmented Generation (RAG) pipelines to ground LLMs with up-to-date and authoritative external data. * Manage the entire data flow for RAG ...
Senior Data Scientist
Olathe, KS · On-site
Lead the design and implementation of end-to-end Retrieval-Augmented Generation (RAG) pipelines to ground LLMs with up-to-date and authoritative external data. * Manage the entire data flow for RAG ...
Software Engineer
Overland Park, KS · On-site
$105K - $120K/yr
Prompt engineering, vector databases, and retrieval-augmented generation (RAG). * IIS configuration and management. * Deployment tooling (Octopus Deploy or similar). * Exposure to containerization ...
Quick apply
Software Engineer
Overland Park, KS · On-site
$105K - $120K/yr
Prompt engineering, vector databases, and retrieval-augmented generation (RAG). * IIS configuration and management. * Deployment tooling (Octopus Deploy or similar). * Exposure to containerization ...
Software Engineer
Overland Park, KS · On-site
$105K - $120K/yr
Prompt engineering, vector databases, and retrieval-augmented generation (RAG). * IIS configuration and management. * Deployment tooling (Octopus Deploy or similar). * Exposure to containerization ...
Quick apply
Software Engineer
Overland Park, KS · On-site
$105K - $120K/yr
Prompt engineering, vector databases, and retrieval-augmented generation (RAG). * IIS configuration and management. * Deployment tooling (Octopus Deploy or similar). * Exposure to containerization ...
Data & AI Engineer
$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
$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
$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.
Quick apply
Data & AI Engineer
$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
$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.
Quick apply
Data & AI Engineer
$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.
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.

Sedgwick rating
7.5
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
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Architect and deploy LLM-powered and agentic AI solutions that transform claims intake, policy interpretation, fraud detection, and resolution workflows.
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Design end-to-end retrieval-augmented generation (RAG) systems leveraging enterprise knowledge bases, policy documents, SOPs, and historical claims data.
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Build autonomous and semi-autonomous agents capable of reasoning, planning, and executing multi-step claims processes.
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Develop stateful workflow orchestration layers that manage context, memory, and task sequencing across interactions.
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Implement planning and reflection loops that decompose complex claims scenarios into structured subtasks.
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Enable dynamic tool use through function calling and secure API integrations with claims systems, CRM platforms, document repositories, and analytics tools.
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Develop document intelligence pipelines using LLMs for summarization, entity extraction, classification, validation, and timeline reconstruction.
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Design structured prompt frameworks that enforce deterministic outputs and domain-aware reasoning.
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Build multi-agent systems that coordinate document review, coverage analysis, compliance checks, and decision support.
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Implement human-in-the-loop checkpoints for escalation, review, and override of AI-driven decisions.
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Develop guardrails, output validation layers, and hallucination mitigation strategies.
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Enforce structured outputs using schemas, type validation, and deterministic post-processing logic.
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Optimize token consumption, inference latency, and cloud infrastructure costs.
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Deploy scalable AI microservices using containerization and cloud-native architectures.
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Implement monitoring for model drift, retrieval quality degradation, reasoning failures, and workflow breakdowns.
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Maintain detailed audit logs of model decisions, agent reasoning steps, and tool executions.
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Develop evaluation frameworks to test reasoning accuracy, workflow completion rates, and system reliability.
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Collaborate with data engineering to build embedding pipelines, feature stores, and vector indexing strategies.
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Ensure compliance with Responsible AI standards, data privacy regulations, and enterprise governance policies.
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Partner with claims operations leadership to embed AI capabilities directly into adjuster and supervisor workflows.
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Measure business impact through cycle-time reduction, automation coverage, fraud detection lift, and operational efficiency gains.
Qualifications
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Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, or related field.
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5+ years of experience building production-grade AI or advanced software systems.
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2-4+ years of hands-on experience with LLM-powered applications and orchestration layers.
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Strong expertise in retrieval-augmented generation architectures and vector search systems.
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Experience designing and implementing multi-agent systems and workflow orchestration engines.
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Deep understanding of planning loops, contextual memory, and tool-augmented LLM reasoning.
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Strong proficiency in Python and API-driven system design.
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Experience integrating enterprise platforms and building secure connectors.
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Familiarity with Azure OpenAI or similar enterprise LLM environments.
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Experience deploying containerized services and managing CI/CD pipelines.
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Understanding of distributed systems, microservices, and event-driven architectures.
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Experience implementing guardrails, access controls, and auditability mechanisms.
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Strong knowledge of evaluation methodologies for LLM reliability and agent performance.
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Experience in insurance, claims, healthcare, or other regulated industries preferred.
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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