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Ai Rag Jobs in Durham, NC (NOW HIRING)

Deloitte

AI Engineer Consultant

Raleigh, NC · Hybrid

We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

Everpure

Senior Technical Evangelist, AI

Raleigh, NC · On-site

$146K - $219K/yr

THE ROLE Accelerate Everpure's AI momentum by transforming breakthrough infrastructure technology ... Practical experience implementing Retrieval-Augmented Generation (RAG) architectures, vector ...

New

Compunnel

Senior Machine Learning Engineer

Raleigh, NC · On-site

$101K - $139K/yr

Required Qualifications: • 7-10+ years of AI/machine learning engineering experience, including building RAG systems end-to-end and deploying solutions through production. • Strong software and ...

Compunnel

Data Scientist

Raleigh, NC · On-site

This role focuses on building and deploying customer-facing AI products, leading RAG implementations, evaluating LLM performance, and scaling production-grade ML systems. The ideal candidate will ...

TEKsystems

Machine Learning Engineer

Raleigh, NC

$75 - $85/hr

You will work closely with Applied Scientists, ML Engineers, Architects, Product Leaders, and Software Engineers to develop production-grade AI systems that leverage Large Language Models (LLMs), RAG ...

New

GEI Consultants

AI Engineer

Cary, NC · On-site

... RAG), and event-driven patterns. * Maintain and optimize Power BI dashboards that support AI ... enabled workflows and insights. * Instrument AI solutions for telemetry, reliability, performance ...

GEI Consultants

AI Engineer

Cary, NC · On-site

$110K - $150K/yr

... RAG), and event-driven patterns. * Maintain and optimize Power BI dashboards that support AI ... enabled workflows and insights. * Instrument AI solutions for telemetry, reliability, performance ...

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All JobsAi Rag JobsAi Rag Jobs in Durham, NC

Ai Rag information

See Durham, NC salary details

$30.9K

$56.3K

$80.7K

How much do ai rag jobs pay per year?

As of Sep 5, 2026, the average yearly pay for ai rag in Durham, NC is $56,283.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,300.00 and $62,800.00 per year, depending on experience, location, and employer.

What is an AI RAG?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

What are the key skills and qualifications needed to thrive as an AI researcher?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What are common challenges faced by AI RAG engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

What are popular job titles related to Ai Rag jobs in Durham, NC?

For Ai Rag jobs in Durham, NC, the most frequently searched job titles are:

What job categories do people searching Ai Rag jobs in Durham, NC look for?

The top searched job categories for Ai Rag jobs in Durham, NC are:

What cities near Durham, NC are hiring for Ai Rag jobs?

Cities near Durham, NC with the most Ai Rag job openings:

Ai Rag jobs near you

AI Engineer (LLMs for Healthcare)

Keebler Health

Durham, NC • On-site

Full-time

Re-posted yesterday

Job description

Job Summary:
Keebler Health is building the operating system for value-based care, aiming to empower healthcare organizations with data-driven insights. The role involves developing and fine-tuning large language models for healthcare applications, collaborating with healthcare professionals, and optimizing AI workflows.
Responsibilities:
• Fine-tune and optimize large language models (LLMs) to address specific healthcare applications.
• Develop and apply advanced prompt engineering techniques to enhance model outputs for clinical scenarios.
• Implement Retrieval-Augmented Generation (RAG) systems to improve knowledge retrieval from large datasets.
• Work with knowledge graphs to organize and integrate healthcare-specific data for enhanced decision-making.
• Evaluate black-box models using precision, recall, and other performance metrics, ensuring robustness and reliability.
• Collaborate with healthcare professionals to understand workflows and identify opportunities for AI-driven enhancements.
• Design and build AI models that align with healthcare standards and regulations (e.g., HIPAA compliance).
• Integrate domain-specific knowledge of healthcare data, including FHIR and interoperability standards, into AI solutions.
• Develop and maintain scalable, production-ready AI pipelines using MLOps tools.
• Deploy and monitor AI models in production environments to ensure performance and compliance.
• Optimize infrastructure for efficient training, testing, and deployment of models.
• Stay at the forefront of advancements in AI, especially in healthcare applications.
• Identify and resolve performance bottlenecks in AI workflows.
• Explore emerging trends and technologies in LLMs and healthcare to continually improve solutions.
• Partner with cross-functional teams, including data engineers and clinicians, to ensure seamless integration of AI into healthcare workflows.
• Communicate technical results and insights effectively to non-technical stakeholders.
Qualifications:
Required:
• Proven experience in LLM fine-tuning and advanced prompt engineering.
• Strong background in Python and modern ML frameworks (e.g., Huggingface, pyTorch).
• Familiarity with healthcare workflows and regulatory requirements (e.g., HIPAA, FHIR standards).
• Hands-on experience with retrieval-augmented generation (RAG) techniques.
• Expertise in evaluating AI models using performance metrics like precision, and recall.
Preferred:
• Experience with MLOps frameworks such as MLflow, Langfuse, or similar tools.
• Understanding of healthcare data standards, including HL7 and HEDIS metrics.
• Strong problem-solving skills in integrating AI with complex healthcare datasets.
• Familiarity with cloud platforms (e.g., AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
Company:
Keebler Health develops an AI-powered healthcare analytics platform focused on risk adjustment and clinical decision support. Founded in 2023, the company is headquartered in Durham, USA, with a team of 11-50 employees. The company is currently Early Stage.

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