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Retrieval Augmented Generation Jobs in Arkansas (NOW HIRING)

AAIT Health

AI Engineer

Little Rock, AR · On-site

Implement retrieval-augmented generation (RAG) and clinical knowledge workflows that query patient context, incorporate medical reference content, and cite sources with traceability. * Engineer ...

AAIT Health LLC

AI Engineer

Little Rock, AR · On-site

Implement retrieval-augmented generation (RAG) and clinical knowledge workflows that query patient context, incorporate medical reference content, and cite sources with traceability. * Engineer ...

Walmart

Principal, Data Scientist

Bentonville, AR · On-site

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

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Retrieval Augmented Generation information

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 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 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.

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Retrieval Augmented Generation jobs near you

AI Engineer

AAIT Health

Little Rock, AR • On-site

Full-time

Re-posted 29 days ago

Job description

Description:

About Us


AAIT Health (Advanced Artificial Intelligence Technology Health) is building a modern, HIPAA-compliant Electronic Medical Records (EMR) platform. We’re focused on turning today’s best AI and LLM capabilities into reliable, secure, production-grade workflows embedded directly into the EMR experience, so clinicians and staff can work smarter, faster, and with less administrative burden.


What You’ll Do


  • Build agentic AI systems that can execute multi-step workflows (e.g., chart review ? summarize ? recommend next actions ? draft documentation ? route tasks) with appropriate human oversight.


  • Design and implement tool-using LLM workflows (function calling / tools, retrieval, structured outputs, planner–executor patterns, and guardrails).


  • Integrate AI capabilities into our EMR via backend services and APIs (e.g., .NET Core services, MySQL, and modern frontend clients).


  • Implement retrieval-augmented generation (RAG) and clinical knowledge workflows that query patient context, incorporate medical reference content, and cite sources with traceability.


  • Engineer safety, privacy, and compliance into AI workflows, including PHI-safe processing, audit logs, role-based access, minimum-necessary data, prompt/data redaction, and secure storage.


  • Evaluate and improve quality using automated and human-in-the-loop evaluation (e.g., grounding, hallucination rates, task success, latency, and cost).


  • Deploy and operate AI services in production, including monitoring, rate limiting, fallbacks, caching, incident response, and model/provider switching.


  • Collaborate cross-functionally with product, clinical SMEs, security/compliance, and engineering to ship AI-powered features that users trust.

Role Details


  • Full-time position with presence in the office required.


  • Core schedule: Monday–Thursday 8:00 a.m. to 5:00 p.m. and Friday 8:00 a.m. to 12:00 p.m., with occasional work outside regular business hours as needed.


  • Travel may be required.


  • The position operates in a professional office environment and involves significant time writing, typing, speaking, listening, standing, sitting, walking, and reaching.


  • Operation of standard office equipment, non-CDL motor vehicles, mobile phones, and related technology is expected.
Requirements:

You Might Be a Fit If


  • You have strong software engineering fundamentals and production experience (APIs, testing, debugging, performance).


  • You have hands-on experience building with LLMs (OpenAI/Anthropic/others), including tool/function calling, structured outputs, and retrieval-augmented generation (RAG).


  • You’ve integrated AI into real products (not just notebooks or demos) and understand the tradeoffs of latency, cost, and quality.


  • You can design for reliability with deterministic interfaces, schema validation, retries, fallbacks, and evaluation.


  • You are comfortable working across backend and data, and optionally some frontend integration.


  • You communicate clearly, enjoy collaborating with cross-functional partners, and can explain complex AI behavior to non-technical stakeholders.


  • You are comfortable operating in a startup-like environment, prioritizing impact and iterating quickly while maintaining quality.


Bonus Points


  • Experience in healthcare, EMR, or clinical workflows; familiarity with standards such as HL7/FHIR.


  • Experience with .NET Core, MySQL, and cloud deployment (Azure is a plus).


  • Familiarity with security and compliance practices such as HIPAA and SOC2-style controls, including RBAC and audit logging.


  • Experience building evaluation pipelines (golden datasets, offline eval, red-teaming, prompt regression tests).


  • Experience with vector databases/search or building retrieval layers over relational and document stores.


  • Knowledge of PHI de-identification, redaction, and safe data handling.


Tech Stack


  • Frontend: React + Vite + TypeScript


  • Backend: .NET Core services


  • Database: MySQL


  • Cloud: Azure, AWS


  • AI: LLM APIs, retrieval/vector search, observability & evaluation tooling