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Ai Rag Jobs in Ohio (NOW HIRING)

Citizens

Sr Data Scientist- Generative AI

Cleveland, OH · On-site

Design, develop, and deploy production-grade Generative AI solutions using LLMs, RAG frameworks, AI agents, and workflow orchestration platforms. * Build intelligent document processing capabilities ...

Total Quality Logistics

AI Architect

Cincinnati, OH · On-site

Experience with embeddings, vector databases, RAG patterns, LangChain, Semantic Kernel and MLflow ... AI Strategy & Enterprise Architecture * Evaluate and recommend AI models, APIs and platforms (e.g ...

Citizens

Sr Data Scientist- Generative AI

Cleveland, OH · On-site

Design, develop, and deploy production-grade Generative AI solutions using LLMs, RAG frameworks, AI agents, and workflow orchestration platforms. * Build intelligent document processing capabilities ...

Citizens

Sr Data Scientist- Generative AI

Columbus, OH · On-site

Design, develop, and deploy production-grade Generative AI solutions using LLMs, RAG frameworks, AI agents, and workflow orchestration platforms. * Build intelligent document processing capabilities ...

TQL

AI Architect

Cincinnati, OH

$60.50 - $79.50/hr

Experience with embeddings, vector databases, RAG patterns, LangChain, Semantic Kernel and MLflow ... AI Strategy & Enterprise Architecture* Evaluate and recommend AI models, APIs and platforms (e.g ...

Citizens

Sr Data Scientist- Generative AI

Columbus, OH · On-site

Design, develop, and deploy production-grade Generative AI solutions using LLMs, RAG frameworks, AI agents, and workflow orchestration platforms. * Build intelligent document processing capabilities ...

Flexjet

Director of AI Engineering

Cleveland, OH · On-site

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

Flexjet

Director of AI Engineering

Cleveland, OH · On-site

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

Flexjet LLC

Director of AI Engineering

Cleveland, OH · On-site

Oversee the production deployment of machine learning and LLM‑powered applications, including RAG solutions, AI copilots, model evaluation frameworks, guardrails, and automated retraining processes.

Flexjet

Director of AI Engineering

Cleveland, OH · On-site

... RAG solutions, AI copilots, model evaluation frameworks, guardrails, and automated retraining processes. · Ensure compliance with responsible AI, security, risk management, data privacy ...

RIT Solutions, Inc.

Senior AI Engineer

Mason, OH · On-site

$98K - $134K/yr

... RAG pipelines over enterprise data using embeddings and vector databases • Build multi-step, tool ... Integrate AI systems with APIs, backend services, and cloud platforms • Establish evaluation ...

hireVouch

Design, architect and build scalable Gen AI solutions, including LLM pipelines, Agentic, MCP, Graph/RAG architectures, and prompt-based applications and emerging tech. * Implement cloud-native ...

US Bank

Lead AI Engineer - Observability

Cincinnati, OH · On-site

$98K - $129K/yr

Retrieval-Augmented Generation (RAG) architectures * Prompt engineering techniques * Agentic AI workflows and orchestration * Build intelligent systems using frameworks such as LangChain, LangGraph ...

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Ai Rag information

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 Ohio?

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

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

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

What cities in Ohio are hiring for Ai Rag jobs?

Cities in Ohio with the most Ai Rag job openings:

Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Ohio as of September 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Remote job distribution.

Sr Data Scientist- Generative AI

Columbus, OH • On-site

Citizens Bank
See more jobs at Citizens Bankexternal

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 11 days ago

Job description


Join a team where innovation meets impact. As a Senior Data Scientist, Generative AI & Agentic Systems, you will help drive the bank's AI transformation by designing, developing, and deploying Large Language Model (LLM) solutions, Retrieval-Augmented Generation (RAG) systems, AI agents, and intelligent automation capabilities. You will work across business, technology, risk, and compliance teams to deliver responsible, scalable, and production-ready GenAI solutions that improve customer experiences, enhance operational efficiency, and create measurable business value.
This role is ideal for an experienced data scientist with strong software engineering and machine learning skills, deep expertise in NLP and Generative AI, and experience developing AI solutions within highly regulated environments.
Key Responsibilities
  • Design, develop, and deploy production-grade Generative AI solutions using LLMs, RAG frameworks, AI agents, and workflow orchestration platforms.
  • Build intelligent document processing capabilities for information extraction, summarization, classification, question answering, and conversational AI applications.
  • Develop agentic workflows capable of autonomous reasoning, task execution, tool utilization, and multi-step decision support.
  • Design and implement retrieval pipelines, vector search architectures, embedding strategies, and knowledge-grounded AI systems.
  • Evaluate and improve LLM performance through prompt engineering, model benchmarking, hallucination reduction, and faithfulness testing.
  • Build scalable AI solutions using modern frameworks and infrastructure including vLLM, LangChain, LangGraph, MLflow, Databricks, Snowflake, and cloud-native platforms.
  • Perform exploratory data analysis, feature engineering, and statistical analysis to support machine learning and GenAI model development.
  • Develop model monitoring, evaluation, and observability frameworks to measure quality, reliability, fairness, and operational performance.
  • Collaborate closely with Model Risk Management (MRM), Compliance, Audit, Legal, and Information Security teams to ensure responsible AI deployment.
  • Create technical documentation, model development artifacts, validation packages, and executive-level presentations.
  • Partner with product managers, engineers, data architects, and business stakeholders to identify and prioritize GenAI opportunities.
  • Stay current with advances in Generative AI, agentic systems, multimodal AI, foundation models, and emerging industry best practices.

Qualifications
Required
  • Ph.D. or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, or a related quantitative field.
  • 7+ years of experience in data science, machine learning, predictive analytics, or artificial intelligence.
  • 4+ years of hands-on experience developing NLP and Generative AI solutions.
  • Strong proficiency in Python and modern software development practices.
  • Experience developing and deploying LLM-based applications using commercial or open-source models.
  • Experience with Retrieval-Augmented Generation (RAG), vector databases, embeddings, and semantic search.
  • Experience with prompt engineering, prompt evaluation, and LLM performance optimization.
  • Strong understanding of machine learning algorithms, deep learning, statistical modeling, and model explainability techniques.
  • Experience working with structured and unstructured data at enterprise scale.
  • Experience collaborating with cross-functional stakeholders and communicating technical concepts to non-technical audiences.
  • Strong knowledge of model governance, validation processes, and documentation standards.

Preferred
  • Experience designing and deploying AI agents and multi-agent systems.
  • Experience with agent orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, CrewAI, Autogen, or similar technologies.
  • Experience serving open-source LLMs using vLLM, Hugging Face, or equivalent inference frameworks.
  • Experience with RAG evaluation frameworks such as RAGAS or other LLM evaluation methodologies.
  • Experience with model monitoring, MLOps, and production AI deployment.
  • Experience with cloud AI platforms such as AWS Bedrock, Azure AI, Databricks, Snowflake Cortex.
  • Experience building document intelligence solutions involving PDFs, OCR, document extraction, knowledge extraction from images, and workflow automation.
  • Experience within banking, financial services, fintech, insurance, or other regulated industries.
  • Experience supporting Model Risk Management (MRM), model validation, audit reviews, or regulatory examinations.
  • Familiarity with MCP (Model Context Protocol), tool calling frameworks, and AI workflow automation platforms.

Technical Skills
Generative AI & LLMs
  • GPT, Claude, Llama and other foundation models
  • Retrieval-Augmented Generation (RAG)
  • AI Agents and Multi-Agent Systems
  • Prompt Engineering and Prompt Optimization
  • Fine-Tuning and Model Adaptation
  • LLM Evaluation and Guardrails
  • Knowledge Retrieval and Vector Search

Programming & Frameworks
  • Python
  • SQL
  • PyTorch
  • TensorFlow
  • Scikit-Learn
  • LangChain
  • LangGraph
  • Hugging Face

Data Platforms & MLOps
  • Experience with cloud-based data, AI, and ML platforms (AWS, SageMaker, Databricks, Snowflake, etc.)
  • Experience with distributed data processing frameworks (Spark / PySpark/Snowpark Snowflake)
  • Experience with ML lifecycle, orchestration, and deployment tools (MLflow, Airflow, CI/CD)
  • Experience with AI-assisted development and model monitoring solutions

NLP & Analytics
  • Text Classification
  • Information Extraction
  • Summarization
  • Topic Modeling
  • Question Answering
  • Sentiment Analysis
  • Explainable AI

Preferred Candidate Profile
The ideal candidate needs to demonstrate success building production-scale GenAI solutions such as RAG platforms, conversational AI systems, document intelligence solutions, AI agents, and automated decision-support systems. They possess strong technical depth, understand governance requirements in regulated industries, and can bridge the gap between cutting-edge AI capabilities and practical business outcomes. This individual is comfortable operating from concept through production deployment while maintaining a strong focus on quality, compliance, explainability, and measurable impact.
Hours & Work Schedule
  • Hours per Week: 40
  • Work Schedule: Monday - Friday
  • Hybrid: 4 days per week on-site, 1 day remote

Pay Transparency
The salary range for this position is $124,000- $165,000 per year, plus an opportunity to earn an annual discretionary bonus. Actual pay is based on various factors including but not limited to the budget, work location, and relevant skills and experience.
We offer competitive pay, comprehensive medical, dental and vision coverage, retirement benefits, maternity/paternity leave, flexible work arrangements, education reimbursement, wellness programs and more. Note, Citizens' paid time off policy exceeds the mandatory, paid sick or paid time-away policy of every local and state jurisdiction in the United States. For an overview of our benefits, visit https://jobs.citizensbank.com/benefits .
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About Us
Equal Employment Opportunity
Citizens, its parent, subsidiaries, and related companies (Citizens) provide equal employment and advancement opportunities to all colleagues and applicants for employment without regard to age, ancestry, color, citizenship, physical or mental disability, perceived disability or history or record of a disability, ethnicity, gender, gender identity or expression, genetic information, genetic characteristic, marital or domestic partner status, victim of domestic violence, family status/parenthood, medical condition, military or veteran status, national origin, pregnancy/childbirth/lactation, colleague's or a dependent's reproductive health decision making, race, religion, sex, sexual orientation, or any other category protected by federal, state and/or local laws. At Citizens, we are committed to fostering an inclusive culture that enables all colleagues to bring their best selves to work every day and everyone is expected to be treated with respect and professionalism. Employment decisions are based solely on merit, qualifications, performance and capability.
Equal Employment and Opportunity Employer
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Background Check
Any offer of employment is conditioned upon the candidate successfully passing a background check, which may include initial credit, motor vehicle record, public record, prior employment verification, and criminal background checks. Results of the background check are individually reviewed based upon legal requirements imposed by our regulators and with consideration of the nature and gravity of the background history and the job offered. Any offer of employment will include further information.

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