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Ai Rag Jobs in Kentucky (NOW HIRING)

Trackmind

$92K - $127K/yr

Key Responsibilities: • Design, develop, and deploy LLM-powered applications using RAG architectures • Build AI agents and multi-agent systems using LangChain and LangGraph • Develop scalable ...

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Purple Drive Technologies

AI Engineer

Louisville, KY · On-site

Experience in Generative AI (LLMs, Prompt Engineering, LangChain, RAG frameworks) . * Exposure to Edge AI , IoT , or Real-time analytics . * Familiarity with API integration and microservices ...

Humana

Principal Applied AI Engineer

Louisville, KY · On-site +1

Lead the design and build of advanced generative AI systems,spanning LLM-powered applications, multi-agent workflows, RAG, and domain-specific reasoning engines. * Architect and own robust APIs and ...

Humana

Principal Applied AI Engineer

Louisville, KY · On-site +1

Lead the design and build of advanced generative AI systems,spanning LLM-powered applications, multi-agent workflows, RAG, and domain-specific reasoning engines. * Architect and own robust APIs and ...

Humana

Principal Applied AI Engineer

Louisville, KY · On-site +1

Lead the design and build of advanced generative AI systems,spanning LLM-powered applications, multi-agent workflows, RAG, and domain-specific reasoning engines. * Architect and own robust APIs and ...

Deloitte

AI Data Engineer - Senior Consultant

Louisville, KY · Hybrid

$100K - $137K/yr

Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ... AI Engineer Senior Consultant Our Deloitte Human Capital team transforms technology platforms ...

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What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

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 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 AI RAGs?

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 some common challenges faced by AI RAG (Retrieval-Augmented Generation) 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.
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What job categories do people searching Ai Rag jobs in Kentucky look for? The top searched job categories for Ai Rag jobs in Kentucky are:
What cities in Kentucky are hiring for Ai Rag jobs? Cities in Kentucky with the most Ai Rag job openings:
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Principal Enterprise Solution Architect, AI Health Cloud

Principal Enterprise Solution Architect, AI Health Cloud

BrightSpring Health Services

Louisville, KY

$218K/yr

Full-time

Posted 29 days ago

BrightSpring Health Services rating

4.6

Company rating: 4.6 out of 10

Based on 60 frontline employees who took The Breakroom Quiz

213th of 228 rated social care providers

Job description

Overview

We are seeking a highly experienced and hands-on Principal Enterprise Architect to lead the full stack design and integration of advanced AI/ML platform and solution architectures within our AI-enabled Health Cloud Platform. This role combines deep expertise in end to end systems architecture, platform engineering, and integration/API design, enabling seamless, secure, and scalable AI/ML healthcare applications.


You will play a critical role in shaping both the technical foundation of the platform and the delivery of intelligent healthcare solutions, ensuring that AI capabilities such as Generative AI, Agentic AI, RPA, and fine-tuned RAG/Vector DB models are effectively embedded into clinical and pharmacy workflows.


Responsibilities

  • Architect and lead the full stack implementation of end-to-end AI/ML platform and solution architectures, ensuring seamless integration with enterprise systems and healthcare data sources.
  • Design and develop robust, scalable APIs and microservices using technologies such as .NET, C#, and RESTful services.
  • Define and enforce platform and integration architecture standards, including data contracts, API security, service orchestration, and interoperability.
  • Collaborate with AI/ML engineers, product managers, API, and data engineers to translate business and clinical requirements into cloud-native, reusable platform capabilities and solution-specific workflows.
  • Architect and optimize LLM-based systems, including RAG pipelines, vector databases, and agentic AI frameworks (e.g., LangChain, AutoGen).
  • Design and evolve the AI/ML platform architecture, including model lifecycle management, orchestration layers, and reusable AI services.
  • Ensure all solutions and platform components are HIPAA-compliant, secure, and aligned with healthcare interoperability standards (FHIR, HL7 etc.).
  • Evaluate and integrate third-party AI services, open-source tools, and cloud-native components into the platform.
  • Provide architectural leadership across model training, deployment, monitoring, and retraining, ensuring scalability and performance.

Qualifications

  • 10+ years of experience in solution and platform architecture, with a strong focus on AI/ML systems and enterprise integration.
  • 15+ years in overall solution architecture.
  • Proven experience designing and delivering AI-powered healthcare or pharmacy platforms and solutions.
  • Experience with multi-agent systems, RPA tools, and intelligent automation in healthcare.
  • Strong experience with API development and integration using .NET, C#, and modern API frameworks.
  • Deep understanding of cloud platforms (Azure preferred), containerization (Docker, Kubernetes), and MLOps practices.
  • Expertise in Generative AI, RAG architectures, vector databases, and agentic AI orchestration.
  • Expertise in healthcare data standards and secure data handling practices.
  • Excellent communication and stakeholder engagement skills.

Preferred Qualifications:

  • Background in AI/ML engineering, data science, or biomedical informatics.
  • Knowledge of responsible AI, model explainability, and bias mitigation.
  • Advanced degree (MS or PhD) in Computer Science, AI/ML, or a related field.

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