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Ai Rag Jobs in Colorado (NOW HIRING)

EchoStar

Senior AI DevOps Engineer

Littleton, CO · On-site

$96K - $137K/yr

Implement complex AI workflows using frameworks like LangChain and LlamaIndex to drive agentic ... Deliver high-quality RAG pipelines utilizing Milvus vector databases to improve data retrieval ...

Pyramid Consulting, Inc

AI/LLM Engineers

Denver, CO · On-site

RAG, MODEL CONTEXT PROTOCOL, LLMs, AWS SERVICES, ETL, SQL. • Hands-on experience building with ... pipelines, and AI agent workflows. • Strong written and verbal communication skills. • ...

Stackinfra

AI Solution Architect

Denver, CO · On-site

The AI Solution Architect is the builderandowns thedataarchitecture, the Python code, the ... Build andoptimizeRetrieval-Augmented Generation (RAG) pipelines end to end - including document ...

Gusto, Inc.

Sr. Staff AI/ML Engineer

Denver, CO · On-site

$245K - $272K/yr

AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively ... This includes developing core platform capabilities - agent orchestration, RAG infrastructure, eval ...

EchoStar

Senior AI DevOps Engineer

Littleton, CO · On-site

$96K - $137K/yr

Implement complex AI workflows using frameworks like LangChain and LlamaIndex to drive agentic ... Deliver high-quality RAG pipelines utilizing Milvus vector databases to improve data retrieval ...

Gusto

Sr. Staff AI/ML Engineer

Denver, CO · On-site +1

$245K - $272K/yr

AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively ... This includes developing core platform capabilities - agent orchestration, RAG infrastructure, eval ...

Apex Systems

AI Engineer

Greenwood Village, CO · On-site +1

LLMs, RAG, agents and agent orchestration (LangChain, Crew AI, etc.) * Software development skills - UIs with React/Typescript, Node.js, Python - she was not sure if both Node.js and Python were ...

Tolmar

AI Engineer I - Hybrid

Windsor, CO · On-site

Implement retrievalaugmented generation (RAG) using enterprise data sources and vector databases. Engineering & Production Readiness * Help transition AI solutions from prototype to production ...

Pyramid Consulting, Inc.

AI/LLM Engineers

Denver, CO

$52 - $56/hr

Immediate need for a talented AI/LLM Engineers. This is a 12+ Months contract opportunity with ... Key skills: RAG, MODEL CONTEXT PROTOCOL, LLMs, AWS SERVICES, ETL, SQL. * Hands-on experience ...

Palnar

ONLY W2 | AI Engineer@ Denver, Co

Denver, CO · On-site

We are looking for highly skilled AI Engineers with expertise in Large Language Models (LLMs) and ... Solid understanding of LLM architectures, embeddings, and retrieval-augmented generation (RAG)

Paradigm

Senior Applied AI Engineer

Denver, CO · On-site

$127K - $187K/yr

Implement and optimize retrieval-augmented generation (RAG) pipelines to connect LLMs with real-time project data, specifications, and plan sets. * Integrate AI models into production services and ...

Deloitte

AI Engineer Consultant

Denver, CO · Hybrid

Implement RAG and document intelligence patterns (ingestion, chunking, embeddings, vector/hybrid ... AI Engineer Consultant Our Deloitte Human Capital team transforms technology platforms, drives ...

Deloitte

AI Engineer III

Denver, CO · Hybrid

Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ... AI Engineer III Position Summary Our Deloitte Human Capital team transforms technology platforms ...

Deloitte

AI Engineer Consultant

Denver, CO · Hybrid

We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

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Ai Rag information

What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

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 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 AI RAGs?

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 some common challenges faced by AI RAG (Retrieval-Augmented Generation) 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 are popular job titles related to Ai Rag jobs in Colorado? For Ai Rag jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Ai Rag jobs in Colorado look for? The top searched job categories for Ai Rag jobs in Colorado are:
What cities in Colorado are hiring for Ai Rag jobs? Cities in Colorado with the most Ai Rag job openings:
Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Colorado as of May 2026, with employment types broken down into 81% Full Time, 6% Part Time, and 13% Contract. Highlights an 100% In-person job distribution.
AI/ML Engineer

AI/ML Engineer

Frontier Technology Inc.

Colorado Springs, CO • Remote

$140K - $220K/yr

Full-time

Posted 16 days ago

Job description

Overview

Frontier Technology Inc. (FTI) is seeking a hands-on AI/ML Engineer to design, build, and deploy advanced machine learning solutions supporting defense and national security missions. This role focuses on execution in oversight, ideal for an engineer who thrives in the code, enjoys building end-to-end pipelines, and takes pride in seeing their work directly impact operational systems.

FTI delivers mission-focused solutions to the Department of Defense/Department of War (DoD/DoW) and Intelligence Community (IC) through advanced engineering, digital transformation, and program execution expertise. We help our customers solve complex challenges and achieve mission success by integrating people, process, and technology.

Responsibilities
  • Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives.
  • Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.
  • Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration).
  • Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid).
  • Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.
  • Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT).
  • Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy.
  • Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots.
  • Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs.
  • Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.
Education/Qualifications

Minimum Requirements:

  • Must be a U.S. citizen and be willing to obtain and maintain a secruity clearance, as needed.
  • 6-10+ years of professional experience developing and deploying AI/ML solutions in production environments.

  • Minimum of 3 years' professional experience within the Department of Defense/Department of War (DoD/DoW) AI assurance, security, and deployment environments.

  • Strong Python development skills with hands-on experience building AI/ML solutions.
  • Direct experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain.
  • Proven ability to build and deploy MLOps pipelines using MLflow, Kubeflow, DVC, or equivalent.
  • Working knowledge of vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval-based architectures (RAG, hybrid, graph).
  • Professional experience fine-tuning and evaluating LLMs or smaller task-specific models using LoRA, QLoRA, or PEFT.
  • Professional experience integrating AI capabilities into production systems or mission applications.

 Preferred Qualifications:

  • Familiarity with agentic frameworks (LangGraph, AutoGen, CrewAI, DSPy) and multi-agent reasoning.
  • Understanding of prompt engineering, retrieval quality, and grounding methods.
  • Exposure to GPU-based or edge inference environments.
  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field.
  • Active Secret clearance preferred; ability to obtain one is required.

For this role, the compensation range is $140k-$220k.

*Note: Starting pay will be based on a number of factors and commensurate with the candidate's residence location, qualifications & experience.

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Employment Type: FULL_TIME

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