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Ai Llm Data Labeling Jobs in Colorado (NOW HIRING)

Pyramid Consulting, Inc

AI/LLM Engineers

Denver, CO · On-site

Job Summary : Pyramid Consulting, Inc. is seeking talented AI/LLM Engineers for a long-term ... SQL and data platforms (Athena, Spark, or equivalent). • Strong software debugging skills ...

IMA Financial Group

AI Engineering Lead

Denver, CO · On-site +1

$105K - $139K/yr

... data workflows and agentic systems • Build and maintain LLM-enabled services, prompt frameworks, and coding standards • Develop semantic/context layers ensuring AI outputs align with business ...

Weld County

AI Solutions Analyst

Greeley, CO

$90K - $127K/yr

Leverage large language model (LLM) orchestration patterns including ReAct, chain-of-thought ... Understanding of ethical AI practices, data privacy regulations, and organizational security ...

Billgo,-Inc.-1

AI Data Scientist

Fort Collins, CO · On-site

$120 - $180/hr

Experience developing LLM-powered assistants or AI copilots for financial operations or support.Strong data storytelling and visualization skills (Tableau preferred). #J-18808-Ljbffr

Weld County

AI Solutions Analyst

Greeley, CO · On-site

$90K - $127K/yr

Leverage large language model (LLM) orchestration patterns including ReAct, chain-of-thought ... Understanding of ethical AI practices, data privacy regulations, and organizational security ...

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Ai Llm Data Labeling information

What is AI LLM data labeling?

AI LLM data labeling is the process of annotating or tagging data—such as text, images, or audio—to provide clear examples that help train large language models (LLMs) like GPT or BERT. This labeled data is essential for teaching models to understand context, intent, and meaning, which improves their performance on various tasks. Data labelers often follow specific guidelines to ensure consistency and accuracy, making their role critical in developing reliable AI systems.

What are some common challenges faced in AI LLM data labeling and how can they be managed?

One common challenge in AI LLM data labeling is ensuring consistency and accuracy when annotating large volumes of complex language data. Labelers often encounter ambiguous or context-dependent text, making it important to follow detailed guidelines and participate in regular calibration sessions with the team. Collaboration with data scientists and project managers is essential to clarify edge cases and refine labeling criteria. Proactively communicating questions and feedback helps maintain high-quality datasets, which are critical for training reliable language models.

What are the key skills and qualifications needed to thrive as an AI LLM data labeling specialist, and why are they important?

To thrive as an AI LLM Data Labeling Specialist, you need keen attention to detail, strong analytical skills, and a foundational understanding of natural language processing concepts, often supported by familiarity with data annotation guidelines. Experience with labeling platforms (such as Labelbox or Prodigy), spreadsheet tools, and sometimes proficiency in scripting languages like Python is highly valued. Excellent communication, consistency, and critical thinking are crucial soft skills for interpreting ambiguous data and ensuring labeling accuracy. These skills and qualifications are vital for producing high-quality training data that directly impacts the performance and reliability of large language models.

What is the difference between Ai Llm Data Labeling vs Data Annotation Specialist?

AspectAi Llm Data LabelingData Annotation Specialist
CredentialsBasic technical skills, familiarity with labeling toolsSimilar technical skills, often with additional domain knowledge
Work EnvironmentData labeling platforms, remote or office settingsData annotation projects, remote or onsite
Industry UsageAI, machine learning, NLP projectsData preparation across various industries including AI

Ai Llm Data Labeling and Data Annotation Specialist roles both involve preparing data for machine learning models. However, Ai Llm Data Labeling typically focuses on labeling data specifically for large language models, requiring familiarity with NLP and AI tools. Data Annotation Specialists may work across broader data types and industries, with a focus on accurate data tagging. Both roles demand similar skills but differ in scope and application within AI projects.

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Ai Llm Data Labeling jobs near you

AI/LLM Engineers

Denver, CO • On-site

Pyramid Consulting, Inc
See more jobs at Pyramid Consulting, Incexternal

Full-time

Re-posted 25 days ago

Job description

Job Summary:
Pyramid Consulting, Inc. is seeking talented AI/LLM Engineers for a long-term contract opportunity. The role involves designing test cases, managing development work, and translating team needs into working software.
Responsibilities:
• Ability to design thorough test cases and support reliability testing across the tools and agents you build.
• Ability to manage your own development work effectively.
• Ability to translate non-technical team needs into working software.
Qualifications:
Required:
• Ability to design thorough test cases and support reliability testing across the tools and agents you build.
• Ability to manage your own development work effectively.
• Ability to translate non-technical team needs into working software.
• Key skills: RAG, MODEL CONTEXT PROTOCOL, LLMs, AWS SERVICES, ETL, SQL.
• Hands-on experience building with LLMs — prompt engineering, agent frameworks, tool-use patterns, or RAG systems.
• Experience with the Model Context Protocol (MCP) or similar agent-tool integration patterns.
• 3+ years of software engineering experience with production applications (TypeScript/JavaScript, Python, or similar).
• Active AWS certification and demonstrated knowledge of AWS services.
• Experience with SQL and data platforms (Athena, Spark, or equivalent).
• Strong software debugging skills, including ability to diagnose issues across distributed systems, data pipelines, and AI agent workflows.
• Strong written and verbal communication skills.
• Experience building knowledge graphs, context graphs, or semantic layers for enterprise data.
• Familiarity with Tableau, data visualization tools, or BI platform APIs.
• Background in data engineering or analytics, understanding ETL pipelines, data quality, and validation workflows.
• Experience with CI/CD, Gitlab, and infrastructure-as-code.
• Familiarity with Jira APIs, Confluence, or project management tool integrations.
• Experience with job monitoring, log analysis, or operational alerting systems.
Company:
Pyramid Consulting, a global leader in workforce and technology solutions, empowers individuals and organizations to transform and thrive in the most challenging and competitive markets. Founded in 1996, the company is headquartered in Alpharetta, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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