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Hourly Large Language Model Llm Jobs in Ohio (NOW HIRING)

Whiz Global LLC

ML Engineer

Columbus, OH · On-site

This role focuses on engineering production-ready machine learning applications, Large Language Model (LLM) solutions, and cloud-native ML platforms while partnering closely with Data Science teams ...

Whiz Global LLC

ML engineer

Columbus, OH · On-site

Experience building and deploying Large Language Model (LLM) solutions. * Hands-on experience with Amazon Bedrock for LLM development is preferred. Responsibilities * Design, build, deploy, and ...

Citizens

Sr Data Scientist- Generative AI

Columbus, OH · On-site

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 ...

Citizens

Sr Data Scientist- Generative AI

Cleveland, OH · On-site

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 ...

KeyCorp

AI-Ready Context Engineer/ Ontologist

Brooklyn, OH · On-site

$96 - $181/hr

... Large Language Model (LLM) use cases. Success in this role requires the ability to translate complex theoretical concepts into scalable, governed information structures that drive adoption of the ...

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Hourly Large Language Model Llm information

What is an hourly large language model LLM?

Hourly Large Language Model (LLM) jobs are roles where individuals work with LLMs, such as ChatGPT or similar AI systems, on an hourly basis. These positions often involve tasks like data annotation, prompt engineering, AI model evaluation, or content generation. Workers may be responsible for improving AI responses, testing models, or creating training data. The 'hourly' aspect means they are paid based on the number of hours worked, rather than a fixed salary or per-project rate. Such jobs are common in tech companies, research organizations, or freelance platforms.

What are the key skills and qualifications needed to thrive as a large language model (LLM) engineer?

To thrive as a Large Language Model (LLM) Engineer, you need a solid background in machine learning, natural language processing, and programming—typically with a degree in computer science or a related field. Experience with frameworks like TensorFlow or PyTorch, familiarity with cloud platforms, and knowledge of model deployment tools are highly valued, along with certifications in AI or data science. Strong problem-solving skills, creativity, and effective communication help you collaborate with cross-functional teams and innovate solutions. These competencies are crucial for developing, optimizing, and scaling LLMs to meet evolving business and research needs.

What are some common challenges faced by hourly large language model (LLM) annotators and how can they be addressed?

Hourly LLM annotators often face challenges such as maintaining consistency in labeling, handling ambiguous or unclear data, and managing the repetitive nature of annotation tasks. To address these challenges, it's helpful to regularly review annotation guidelines, participate in team discussions to clarify uncertainties, and leverage available feedback from quality assurance checks. Collaborating with teammates and project managers can also provide support and ensure alignment on task expectations, making the work environment more collaborative and improving overall accuracy.

What is the difference between Hourly Large Language Model Llm vs Data Scientist?

AspectHourly Large Language Model LlmData Scientist
Required CredentialsKnowledge of AI, NLP, programming skillsDegree in Data Science, Statistics, or related field
Work EnvironmentTech companies, AI research labs, freelance projectsCorporate, consulting firms, research institutions
Industry UsageDeveloping and fine-tuning language models, AI applicationsData analysis, predictive modeling, data visualization

While both roles involve working with data and advanced technology, Hourly Large Language Model Llm focuses on developing and deploying AI language models, whereas Data Scientists analyze data to inform business decisions. The roles share skills in programming and data handling but differ in their primary objectives and work environments.

What are the most commonly searched types of Large Language Model Llm jobs in Ohio?

The most popular types of Large Language Model Llm jobs in Ohio are:

What are popular job titles related to Hourly Large Language Model Llm jobs in Ohio?

For Hourly Large Language Model Llm jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Hourly Large Language Model Llm jobs in Ohio look for?

The top searched job categories for Hourly Large Language Model Llm jobs in Ohio are:

What cities in Ohio are hiring for Hourly Large Language Model Llm jobs?

Cities in Ohio with the most Hourly Large Language Model Llm job openings:

Hourly Large Language Model Llm jobs near you
Infographic showing various Hourly Large Language Model Llm job openings in Ohio as of August 2026, with employment types broken down into 1% As Needed, 58% Full Time, 34% Part Time, 1% Temporary, 5% Contract, and 1% Nights. Highlights an 99% Physical, and 1% Remote job distribution.

ML Engineer

Whiz Global LLC

Columbus, OH • On-site

Other

Posted 29 days ago

Job description

Job Title: ML Engineer (AI/LLM & Cloud)

Location: Jersey City, NJ (Hybrid – 3 Days Onsite) OR Columbus, OH (5 Days Onsite)
Duration: Contract-to-Hire

Job Summary

We are seeking a highly experienced ML Engineer to design, build, deploy, and integrate enterprise-scale AI/ML solutions within the JPMorgan Chase ecosystem. This role focuses on engineering production-ready machine learning applications, Large Language Model (LLM) solutions, and cloud-native ML platforms while partnering closely with Data Science teams and business stakeholders.

The ideal candidate is a hands-on engineer with strong Python expertise, cloud experience, and MLOps knowledge who can lead technical initiatives and deliver scalable AI solutions.

Key Responsibilities
  • Design, develop, deploy, and maintain scalable AI/ML applications.
  • Build production-ready machine learning systems and infrastructure.
  • Productionize machine learning models developed by Data Science teams.
  • Design and deploy Large Language Model (LLM) applications.
  • Integrate AI solutions into AWS and JPMorgan Chase internal cloud platforms.
  • Develop scalable model serving and inference pipelines.
  • Implement CI/CD pipelines for ML applications.
  • Build monitoring, observability, model drift detection, and automated retraining solutions.
  • Optimize AI applications for performance, scalability, reliability, and cost.
  • Collaborate with Product Managers, Data Scientists, Software Engineers, and Business SMEs.
  • Mentor junior engineers and provide technical leadership through architecture guidance and code reviews.
  • Research and implement modern AI/ML technologies and best practices.
Required Qualifications
  • Bachelor''s or Master''s degree in Computer Science, Engineering, Data Science, or a related field.
  • 10+ years of hands-on experience developing and deploying machine learning solutions in production.
  • Expert-level programming experience with Python.
  • Basic understanding of Java or Scala.
  • Strong experience with software engineering principles, data structures, algorithms, and distributed systems.
  • Extensive experience with AWS Cloud.
  • Experience deploying applications across public cloud and enterprise cloud platforms.
  • Hands-on experience with Docker and Kubernetes.
  • Strong experience with MLOps tools including MLflow, Kubeflow, SageMaker, or Vertex AI.
  • Experience implementing CI/CD pipelines for machine learning applications.
  • Experience designing scalable ML infrastructure and distributed data processing systems.
  • Proven ability to lead technical initiatives and mentor engineering teams.
Preferred Qualifications
  • Experience building and deploying Large Language Model (LLM) solutions.
  • Hands-on experience with Amazon Bedrock.
  • Experience with Generative AI applications.
  • Strong understanding of TensorFlow, PyTorch, or Scikit-learn.
  • Experience with Deep Learning, NLP, or Computer Vision.
  • Experience with distributed model training and high-throughput inference systems.
  • Knowledge of model optimization and AI performance tuning.

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