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Entry Level Retrieval Augmented Generation Jobs in Ohio

TekLeaders, Inc

Data Architect

Columbus, OH · On-site

$59.50 - $76.50/hr

... LLMs), Retrieval-Augmented Generation (RAG), Vector Databases, MLflow, KubeflowCloud & Data TechnologiesPython, SQL, API Integration, Kubernetes, Docker, CI/CD, Apache Spark, Databricks ...

New

Tata Consultancy Service Limited

Engineer

Cleveland, OH · On-site

$90K - $150K/yr

... Retrieval-Augmented Generation (RAG), prompt engineering, summarization, and intent classification techniques. • Integrate AI solutions with enterprise applications, ITSM tools, and business data ...

Tata Consultancy Service Limited

Engineer

Cleveland, OH · On-site

$90K - $120K/yr

... Retrieval-Augmented Generation (RAG), prompt engineering, summarization, and intent classification techniques. • Integrate AI solutions with enterprise applications, ITSM tools, and business data ...

HYDAC International

$79.89 - $125.54/hr

* Entwicklung und Integration von AI-Lösungen auf Basis Microsoft Foundry und Power Plattform * Aufbau von Retrieval-Augmented-Generation-Lösungen, Document AI-Lösungen und Agentic AI-Lösungen ...

ESPRiT Engineering GmbH

$76.14 - $99.57/hr

Kenntnisse im Bereich RAG (Retrieval-Augmented Generation) und im Umgang mit Vektordatenbanken (z. B. ChromaDB, Pinecone, Weaviate, Milvus) wünschenswert * Erfahrung mit Graph-Datenbanken (z. B.

HYDAC International

$102.72 - $148.37/hr

Deployment, Versionierung, Monitoring, Skalierung und Fehlerbehebung von Modellen, Prompts, Skills und Agents * Aufbau von Retrieval-Augmented-Generation-Lösungen, Document AI-Lösungen und Agentic ...

Incedo Inc

Senior Machine Learning Engineer

Cincinnati, OH · On-site

$100K - $137K/yr

Hands-on experience developing and deploying Generative AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and modern AI development ...

SassyClassy

$69.41 - $92.55/hr

Was werde ich machen? * Entwicklung von Custom AI Agents und autonomen Workflows (Claude Code, LangChain, CrewAI) * Aufbau und Optimierung von RAG-Systemen (Retrieval-Augmented Generation) für ...

salesHAX Consulting GmbH

$70 - $100/hr

Das ist kein generischer Chatbot, sondern ein präzises Retrieval-Augmented Generation (RAG) System, das ausschließlich auf den verifizierten Inhalten unseres Klienten basiert. Aufgaben RAG-Pipeline ...

UNION HOME MORTGAGE

AI Engineer Intern

Strongsville, OH · On-site

In this role, you will apply foundational knowledge of machine learning, neural networks, Transformer architectures, large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI ...

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Entry Level Retrieval Augmented Generation information

What is an entry level retrieval augmented generation job?

Entry level retrieval augmented generation jobs involve assisting in the development and optimization of AI systems that combine information retrieval techniques with generative models. Employees in these roles typically help build, test, and maintain systems where AI retrieves relevant data from large databases to enhance the accuracy and relevance of generated responses. These positions often require basic skills in programming, machine learning, and familiarity with natural language processing. They are ideal for recent graduates or those new to AI, offering opportunities to learn about modern AI architectures and contribute to innovative projects. Entry level workers may work under the guidance of senior engineers or researchers, supporting experimentation and evaluation tasks.

What are the key skills and qualifications needed to thrive as an entry level retrieval augmented generation specialist?

To thrive as an Entry Level Retrieval Augmented Generation Specialist, you need a foundational understanding of natural language processing (NLP), information retrieval, and basic programming skills, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, vector databases (like FAISS or Pinecone), and frameworks for large language models (LLMs) is typically required. Strong problem-solving abilities, attention to detail, and effective communication help you collaborate and troubleshoot solutions in team environments. These skills and qualities are crucial for building reliable RAG systems that deliver accurate and relevant information to users.

What are some common challenges faced by entry-level professionals working in retrieval augmented generation roles?

Entry-level professionals in Retrieval Augmented Generation (RAG) often encounter challenges such as understanding how to effectively combine information retrieval systems with large language models and adapting to rapidly evolving technologies. Balancing accuracy and efficiency when designing or fine-tuning retrieval pipelines can also be a learning curve. Additionally, you may need to collaborate closely with data engineers, machine learning specialists, and product teams to ensure the RAG system aligns with business requirements. Staying proactive in learning and engaging with peers can help overcome these challenges and accelerate career growth.

What is the difference between Entry Level Retrieval Augmented Generation vs Entry Level Data Scientist?

AspectEntry Level Retrieval Augmented GenerationEntry Level Data Scientist
Required CredentialsBasic programming, understanding of NLP and AI conceptsBachelor's in Data Science, Computer Science, or related field
Work EnvironmentTech companies, AI startups, research labsTech firms, finance, healthcare, consulting
Industry UsageAI development, NLP applications, chatbot creationData analysis, predictive modeling, data-driven decision making

Entry Level Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with generative AI, requiring knowledge of NLP and programming. Entry Level Data Scientist involves analyzing data, building models, and deriving insights, often with a broader data analysis skill set. While both roles require technical skills, Retrieval Augmented Generation is more specialized in AI model development, whereas Data Scientists work across various data projects.

What are the most commonly searched types of Retrieval Augmented Generation jobs in Ohio?

The most popular types of Retrieval Augmented Generation jobs in Ohio are:

What are popular job titles related to Entry Level Retrieval Augmented Generation jobs in Ohio?

For Entry Level Retrieval Augmented Generation jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Entry Level Retrieval Augmented Generation jobs in Ohio look for?

The top searched job categories for Entry Level Retrieval Augmented Generation jobs in Ohio are:

What cities in Ohio are hiring for Entry Level Retrieval Augmented Generation jobs?

Cities in Ohio with the most Entry Level Retrieval Augmented Generation job openings:

Entry Level Retrieval Augmented Generation jobs near you
Infographic showing various Entry Level Retrieval Augmented Generation job openings in Ohio as of August 2026, with employment types broken down into 9% Internship, and 91% Full Time. Highlights an 73% In-person, and 27% Remote job distribution.

AI/ML Engineer

Cincinnati, OH • On-site

Luma Financial Technologies, LLC
Finance and Insurance • 11 - 50 employees
Learn more about Luma Financial Technologies, LLCexternal

$100 - $130/hr

Other

Posted 8 days ago

Job description

Founded in 2018, Luma Financial Technologies (“Luma”) has pioneered a cutting‑edge fintech software platform that has been adopted by broker/dealer firms, RIA offices, and private banks around the world. By using Luma, institutional and retail investors have a fully customizable, independent, buy‑side technology platform that helps financial teams more efficiently learn about, research, purchase, and manage alternative investments as well as annuities. Luma gives these users the ability to oversee the full, end‑to‑end process lifecycle by offering a suite of solutions. These include education resources and training materials; creation and pricing of custom structured products; electronic order entry; and post‑trade management. By prioritizing transparency and ease of use, Luma is a multi‑issuer, multi‑wholesaler, and multi‑product option that advisors can utilize to best meet their clients’ specific portfolio needs. Headquartered in Cincinnati, OH, Luma also has offices in New York, NY, Miami, FL, Zurich, Switzerland and Lisbon, Portugal. For more information, please visit Luma’s website.

About the role

We are seeking a talented AI/ML Engineer with a minimum of 3 years of hands‑on experience in the AI/ML domain. This exciting opportunity, based in India and reporting to the Software Development Manager, is your chance to build AI solutions from the ground up, working alongside the largest global clients in our industry. The ideal candidate will have a robust background in developing and deploying advanced machine learning models and generative AI (GenAI) solutions. You will play a pivotal role in integrating cutting‑edge AI/ML capabilities—ranging from GenAI, Agents, ML Models, and Prompting Techniques to OCR, Vector Databases, and Retrieval Augmented Generation (RAG)—into our platform. Experience with AWS infrastructure, including AWS SageMaker and Bedrock, is considered a plus. More importantly, we’re looking for someone who is eager to experiment, innovate and deliver impactful AI‑driven solutions.

What you’ll do
  • Lead AI Innovation in Fintech:
  • Design, develop, and deploy advanced AI/ML solutions that power the next generation of financial technology.
  • Implement GenAI, agent‑based systems, and sophisticated ML models to enhance our platform capabilities.
  • Own the Full AI Lifecycle:
  • Design and implement robust data models that support AI/ML initiatives.
  • Architect data pipelines to ensure seamless data integration and processing as part of the solution.
  • Collaborate with cross‑functional teams to integrate AI/ML functionalities into our multi‑product, multi‑issuer platform.
  • Develop scalable machine learning pipelines and data processing workflows.
  • Scale AI in the Cloud:
  • Build, test, and optimize AI models on various cloud platforms; AWS experience (including SageMaker and Bedrock) is a bonus.
  • Ensure robust deployment practices and maintain the performance and scalability of AI systems.
  • Specialized Projects:
  • Architect and implement an Agentic Framework tailored specifically for the needs of Financial Advisors, enabling autonomous reasoning, planning, and execution across complex financial scenarios.
  • Develop and enhance OCR capabilities and integrate these with vector databases.
  • Utilize Retrieval Augmented Generation (RAG) techniques to improve data retrieval and decision‑making processes.
  • MLOps Integration (Plus):
  • Champion MLOps best practices to streamline the continuous integration, delivery, and deployment of machine learning models.
  • Collaborate with DevOps teams to optimize and monitor production‑level AI/ML solutions.
  • Be a Thought Leader:
  • Provide technical guidance and mentorship to team members.
  • Engage in knowledge‑sharing sessions to drive continuous improvement across the team.
  • Stay abreast of the latest advancements in AI/ML research, tools, and best practices.
  • Experiment with novel prompting techniques and refine model architectures for improved outcomes.
Qualifications
  • Experience:
  • Minimum 3 years of professional experience in AI/ML engineering or a related field.
  • Must have hands‑on experience working on a commercial product that is already in production.
  • Technical Expertise:
  • In‑depth knowledge of GenAI, agent‑based systems, ML models, and prompting techniques.
  • Practical experience with OCR technologies, vector databases, and Retrieval Augmented Generation (RAG).
  • Proficient in programming languages such as Python and familiar with machine learning libraries (e.g., TensorFlow, PyTorch, scikit‑learn).
  • Experience with cloud platforms is beneficial; AWS experience (specifically with AWS SageMaker and Bedrock) is a plus but not required.
  • Soft Skills:
  • Strong problem‑solving abilities.
  • Excellent communication skills and a collaborative mindset.
  • Ability to thrive in a fast‑paced, innovative environment.
  • Education:
  • Advanced degree (Master’s or PhD) in Computer Science, Data Science, Machine Learning, or a related discipline.
  • Financial Technology Exposure:
  • Experience in the financial technology sector, particularly with structured products or annuities.
  • Additional Technical Skills:
  • Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.
  • Experience with DevOps best practices and contributing to open‑source projects.
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