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Llm Ml Rag Jobs in Kentucky (NOW HIRING)

RiskForce

$120 - $155/hr

Design and implement LLM-powered features and intelligent workflows that solve real customer ... Build and optimize retrieval-augmented generation (RAG) pipelines including document ingestion ...

New

Hippocratic AI Inc.

$190 - $260/hr

Advanced hands-on experience with LLM frameworks (LangChain, LangSmith); deep understanding of RAG ... Experience mentoring engineers or leading technical initiatives; contributions to AI/ML open source ...

AI Chopping Block

$140 - $210/hr

Advanced hands-on experience with LLM frameworks (LangChain, LangSmith); deep understanding of RAG ... Experience mentoring engineers or leading technical initiatives; contributions to AI/ML open source ...

Jade Global

$120 - $160/hr

Develop integrations between ServiceNow, AI/ML services, vector/graph databases, and external ... Strong experience with LangChain and its components for LLM/RAG application development.

Infosys Limited

$154 - $193/hr

... ML solutions across multiple domains, including: * Generative AI and Large Language Model (LLM ... RAG) architectures. * Experience with MLOps/LLMOps practices, including model lifecycle management ...

Jobzhr

$170 - $210/hr

... LLM / ML features * Strong hands-on coding skills in Python; comfortable across the full stack when ... Experience building agentic systems or production RAG at scale Interview Process * Recruiter Screen ...

Hyundai Autoever America

$103 - $159/hr

Proven experience delivering production‑ready AI systems, including LLM-based applications, traditional ML models, RAG pipelines, and agentic/automated workflows. * Hands‑on expertise with modern ...

New

Neura Market

$140 - $200/hr

AI/ML: FastAPI, Pydantic; multi-provider LLM SDKs (Anthropic, OpenAI, and others) * Agentic Tooling ... Advanced RAG expertise -- GraphRAG, agentic RAG, contextual retrieval, reranking strategies * Graph ...

Capgemini

$150 - $160/hr

Lead the development and deployment of AI/ML and Generative AI solution ... Drive implementation of machine learning models, LLM-powered applications, RAG solutions, and ...

Valid8 Financial, Inc.

$140 - $210/hr

Lead decisions around foundation models, fine‑tuning strategies, RAG pipelines, embeddings, and ... Architect and oversee scalable LLM/GenAI systems for MarTech/AdTech use cases * Design and deploy ...

Evidently Ltd.

$250 - $350/hr

You've grappled with LLM failure modes. You know the difference between a benchmark that flatters a ... Design and improve our approach to RAG, prompt engineering, fine-tuning, and model selection with ...

New

Jobzhr

$180 - $240/hr

Ensure AI/ML systems comply with security standards and best practices, addressing data privacy and protection concerns across all LLM integrations, RAG pipelines, and credential-handling systems

INflow, LLC.

$149 - $187/hr

... ML to help our customers lead in a rapidly evolving defense landscape. We empower both our ... In this role, you will develop LLM-enabled workflows and Retrieval-Augmented Generation (RAG ...

New

CACI International Inc.

$75 - $158/hr

Experience with LLM orchestration frameworks such as LangGraph, Agno, CrewAI, or similar * Experience with RAG architecture, agentic patterns, and vector databases * Strong understanding of ML ...

New

carbonhouse, inc.

$92 - $115/hr

... RAG). Charlotte, NC (hybrid -- onsite 3 days/week) $92,000 - $115,000 carbonhouse is looking for a ... Integrate AI/ML capabilities into applications -- LLMs, embeddings, classification and more

Aktra

$180 - $240/hr

Design, develop, and deploy AI/ML solutions for financial applications * Build and optimize LLM ... Experience with RAG architectures, LLM fine-tuning, and AI agents * Knowledge of quantitative ...

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Llm Ml Rag information

What is an llm ml rag job?

LLM ML RAG jobs involve working with Large Language Models (LLMs), Machine Learning (ML), and Retrieval-Augmented Generation (RAG) systems. Professionals in these roles typically design, develop, and optimize AI systems that combine language models with retrieval techniques to improve accuracy, relevance, and factual grounding in generated outputs. These jobs often require expertise in natural language processing, deep learning, data engineering, and information retrieval. Key responsibilities might include integrating RAG pipelines, fine-tuning LLMs, and ensuring high-quality responses from AI applications.

What are some typical challenges faced when working on retrieval-augmented generation (RAG) systems in large language model (LLM) machine learning roles?

Professionals working on LLM ML RAG systems often encounter challenges such as ensuring the accuracy and relevancy of retrieved documents, managing latency for real-time queries, and seamlessly integrating retrieval mechanisms with generation models. Additionally, keeping up with evolving datasets and maintaining high-quality knowledge bases can be demanding. Collaboration with data engineers and domain experts is common to refine retrieval pipelines and optimize the end-to-end system.

What are the key skills and qualifications needed to thrive as an llm ml rag engineer, and why are they important?

To excel as an LLM ML RAG Engineer, you need a strong background in machine learning, natural language processing, and large language models, typically supported by a degree in computer science or a related field. Proficiency with tools and frameworks like Python, PyTorch/TensorFlow, Hugging Face Transformers, and vector databases (e.g., FAISS, Pinecone) is essential, along with experience in deploying and fine-tuning LLMs and integrating retrieval systems. Strong problem-solving skills, attention to detail, and the ability to collaborate with cross-functional teams distinguish top performers in this role. These skills ensure the effective development and deployment of advanced AI solutions that combine generative and retrieval capabilities for high-impact applications.

What is the difference between Llm Ml Rag vs Data Scientist?

AspectLlm Ml RagData Scientist
Required CredentialsMaster's or PhD in ML, AI, or related fields; certifications in ML frameworksDegree in Computer Science, Statistics, or related; certifications in data analysis or ML
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, product development teams
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, tech, consulting firms
Common Search & ComparisonOften compared for ML specialization and research focusCompared for data analysis, modeling, and business insights

While both roles involve working with machine learning, Llm Ml Rag typically focuses on research and development of large language models, requiring advanced ML expertise. Data Scientists often work on analyzing data, building predictive models, and deriving insights for business decisions. The roles overlap in skills but differ in focus and application areas.

What are popular job titles related to Llm Ml Rag jobs in Kentucky?

For Llm Ml Rag jobs in Kentucky, the most frequently searched job titles are:

What job categories do people searching Llm Ml Rag jobs in Kentucky look for?

The top searched job categories for Llm Ml Rag jobs in Kentucky are:

What cities in Kentucky are hiring for Llm Ml Rag jobs?

Cities in Kentucky with the most Llm Ml Rag job openings:

Llm Ml Rag jobs near you
Infographic showing various Llm Ml Rag job openings in Kentucky as of June 2026, with employment types broken down into 94% Full Time, 3% Part Time, and 3% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution.

AI/ML Engineer

On-site

RiskForce
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$120 - $155/hr

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Posted yesterday

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Job description

About the Role

Join our engineering team at the forefront of applied AI to design and build production-grade ML features that power the core product experience. You'll shape the future of how our platform leverages large language models, retrieval-augmented generation, and intelligent automation — working across the full lifecycle from rapid prototyping to reliable, scalable production systems.

What You'll Do
  • Design and implement LLM-powered features and intelligent workflows that solve real customer problems
  • Build and optimize retrieval-augmented generation (RAG) pipelines including document ingestion, chunking strategies, embedding models, and vector search
  • Develop and refine prompt engineering strategies across multiple foundation models and use cases
  • Create evaluation frameworks and benchmarking infrastructure to systematically measure model performance, accuracy, and cost-effectiveness
  • Implement guardrails, output validation, and safety filtering to ensure reliable and trustworthy AI behavior in production
  • Monitor and optimize token usage, latency, and inference costs across multi-model architectures
  • Collaborate closely with product and engineering teams to identify high-impact AI opportunities and translate them into shipped features
Requirements
  • 3+ years of experience in machine learning or AI engineering, with hands-on experience building LLM-powered applications
  • Strong proficiency in Python with production-level software engineering practices
  • Experience building RAG systems with vector databases (Pinecone, Weaviate, pgvector, or similar) and document processing pipelines
  • Solid understanding of NLP fundamentals, prompt engineering, function calling, and tool-use patterns
  • Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex, or equivalent
  • Experience with cloud platforms (AWS, Azure, or GCP) for deploying and scaling ML workloads
  • Ability to design evaluation criteria and measure AI output quality systematically
Nice to Have
  • Experience building AI systems in regulated, high-security, or compliance-driven environments
  • Background in MLOps — model versioning, experiment tracking, CI/CD for ML pipelines
  • Hands-on experience fine-tuning or distilling open-source models (LLaMA, Mistral, etc.)
  • Experience with multi-agent frameworks, autonomous agent architectures, or tool-use orchestration
  • Published research or technical writing in NLP, information retrieval, or applied ML
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