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Llm Ml Rag Jobs in Ohio (NOW HIRING)

Hudson Manpower

Data Scientist

Cincinnati, OH · On-site

$120 - $150/hr

Develop and integrate Generative AI solutions including RAG, prompt engineering, LLM workflows, fine-tuning, and agentic AI where applicable. * Evaluate emerging AI/ML technologies for production ...

Flexjet

Senior Data Scientist

Cleveland, OH · On-site

... Deploy AI/ML models into production environments • Implement model monitoring, performance ... LLM-powered applications in enterprise environments • Hands-on experience with RAG pipelines ...

Flexjet

Director of AI Engineering

Cleveland, OH

... ML operations. Oversee the production deployment of machine learning and LLM-powered applications, including RAG solutions, AI copilots, model evaluation frameworks, guardrails, and automated ...

Flexjet

Director of AI Engineering

Cleveland, OH · On-site

... ML operations. • Oversee the production deployment of machine learning and LLM-powered applications, including RAG solutions, AI copilots, model evaluation frameworks, guardrails, and automated ...

Flexjet

Director of AI Engineering

Cleveland, OH · On-site

... ML operations. · Oversee the production deployment of machine learning and LLM-powered applications, including RAG solutions, AI copilots, model evaluation frameworks, guardrails, and automated ...

Photon

Senior Java Developer - GenAI

Columbus, OH · On-site

$53.50 - $68.25/hr

Design and implement GenAI/LLM-based architectures , including RAG, AI agents, model integration, and AI-powered enterprise solutions. * Work with Python for AI/ML integrations, automation, data ...

New

Flexjet

Senior Data Scientist

Cleveland, OH · On-site

$120 - $180/hr

Design and implement Retrieval‑Augmented Generation (RAG) pipelines * Develop solutions for ... Evaluate and benchmark machine learning and LLM model performance * Work with large‑scale ...

Flexjet

Director of AI Engineering

Cleveland, OH · On-site

$180 - $300/hr

Experience designing and supporting multi‑tenant AI/ML platforms. * Familiarity with RAG architectures, vector databases, and LLM evaluation frameworks. * Experience managing GPU infrastructure and ...

Flexjet LLC

Director of AI Engineering

Cleveland, OH · On-site

$180 - $260/hr

Experience designing and supporting multi‑tenant AI/ML platforms. * Familiarity with RAG architectures, vector databases, and LLM evaluation frameworks. * Experience managing GPU infrastructure and ...

Deloitte

AI Engineer Consultant

Columbus, OH · Hybrid

... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

Deloitte

AI Engineer Consultant

Cleveland, OH · Hybrid

... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

Deloitte

AI Engineer Consultant

Cincinnati, OH · Hybrid

... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

Lorven Technologies

AI Engineer

Columbus, OH · On-site

... ML solutions, developing LLM-powered applications, and working with modern cloud platforms in an ... and RAG architectures. * Experience building AI-powered applications using vector databases ...

Lorven Technologies, Inc.

AI Engineer

Columbus, OH · On-site

... ML solutions, developing LLM-powered applications, and working with modern cloud platforms in an ... and RAG architectures. * Experience building AI-powered applications using vector databases ...

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Llm Ml Rag information

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 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 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 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 popular job titles related to Llm Ml Rag jobs in Ohio?

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

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

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

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

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

Llm Ml Rag jobs near you

Data Scientist

Hudson Manpower

Cincinnati, OH • On-site

$120 - $150/hr

Other

Posted 7 days ago

Job description

Job Summary

We are seeking an experienced Data Scientist to drive causal inference, experimentation, measurement, personalization, and applied AI initiatives. The ideal candidate will have hands-on experience applying causal inference and econometric techniques to measure business impact, build production-ready machine learning solutions, and translate analytical insights into measurable business outcomes. Experience with Generative AI is a plus but not the primary requirement.


Key Responsibilities

  • Design and implement causal inference and causal machine learning solutions.


  • Measure the impact of business treatments on customer behavior, revenue, retention, and engagement.


  • Apply statistical methods including:

    • Difference-in-Differences

    • Matching

    • Panel Data Models

    • CATE Estimation

    • Uplift Modeling

    • Heterogeneous Treatment Effect Modeling


  • Define treatments, control groups, counterfactuals, outcome metrics, and evaluation windows.


  • Build scalable, production-ready ML pipelines using software engineering and MLOps best practices.


  • Partner with business and product teams to convert business problems into scientific solutions.


  • Develop and integrate Generative AI solutions including RAG, prompt engineering, LLM workflows, fine-tuning, and agentic AI where applicable.


  • Evaluate emerging AI/ML technologies for production adoption.


  • Present technical findings and business impact to both technical and non-technical stakeholders.


  • Provide technical guidance and code reviews to team members.



Required Qualifications

  • 3+ years of applied Data Science experience.


  • Strong experience with causal inference, causal ML, econometrics, or experimentation.


  • Experience measuring treatment effects and incremental business impact.


  • Hands-on experience with:

    • Difference-in-Differences

    • Matching

    • CATE

    • Panel Data Analysis

    • Uplift Modeling

    • Heterogeneous Treatment Effects


  • Strong Python and SQL programming skills.


  • Experience with Git.


  • Experience developing production-quality ML or analytics solutions.


  • Strong analytical, communication, and problem-solving skills.


  • Bachelor's or Master's degree in Statistics, Economics, Data Science, Computer Science, Applied Mathematics, or related quantitative field.



Preferred Qualifications

  • Experience with Generative AI, RAG, Prompt Engineering, Fine-tuning, LLM Evaluation, or Agentic AI.


  • Experience with Azure, Databricks, or similar cloud platforms.


  • Experience with MLOps, deployment, orchestration, monitoring, and model lifecycle management.


  • Experience building experimentation platforms or measurement pipelines.


  • Retail, CPG, media, personalization, loyalty, or customer analytics experience.


  • Experience mentoring technical teams.


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