ZipRecruiter

Log In

1

Ai Rag Jobs in Colorado (NOW HIRING)

Ledgent Tech

AI Solutions Architect

Denver, CO · Hybrid

$64.75 - $85.50/hr

If you have deep experience with GenAI, LLMs, RAG pipelines, and agentic systems in real production environments, this role is built for you. What You Will Do Build and Scale Enterprise AI Solutions

Maven Companies

AI Solution Architect (Onsite Role)

Denver, CO · On-site

$64.75 - $85.50/hr

Translate business needs → AI solution design (RAG, agents, ML models) * Lead cross-functional execution (engineering, data, product, design) * Own end-to-end lifecycle (discovery → build → ...

Infosys

Gen AI / Agentic AI Lead

Denver, CO · On-site

$144.10K - $177K/yr

Infosys is seeking a hands-on Gen AI / Agentic AI Lead to drive the development and deployment of next-generation AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG ...

Connect for Health Colorado

AI Developer/Engineer

Denver, CO · On-site

$111.24K - $145.23K/yr

Implement and maintain retrieval-augmented generation (RAG) solutions, including document ingestion, chunking, embeddings, vector search, and prompt orchestration. * Integrate AI applications with ...

Programmers.io

AI Engineering

Denver, CO · On-site

8+ years of management experience 4+ years of LLM experience (fine-tuning, RAG, prompt engineering, agentic) 8+ years of ML/Data Science Experience Someone who has been delivering AI/ML models into ...

Connect for Health Colorado

AI Developer/Engineer

Denver, CO · On-site +1

$111.24K - $145.23K/yr

Implement and maintain retrieval-augmented generation (RAG) solutions, including document ingestion, chunking, embeddings, vector search, and prompt orchestration. * Integrate AI applications with ...

EchoStar Corporation

Senior AI DevOps Engineer

Littleton, CO · On-site

$127.50K - $163.80K/yr

... RAG pipelines and autonomous remediation tools to ensure high-performance, intelligent system reliability across cloud environments. • Implement complex AI workflows using frameworks like LangChain ...

Strive Health

Lead, AI Software Engineer

Denver, CO

This role focuses on helping every engineer at Strive design, build, and ship AI-enabled software safely and effectively - from AI-assisted development workflows to agentic and RAG-based applications ...

InterSources

AI Solutions Architect

Denver, CO

$64.75 - $85.50/hr

Designing GenAI solutions (RAG, copilots, conversational AI) * Solution Architecture (End-to-End) * Designing scalable architectures across data, APIs, and UI * Microservices, event-driven, and cloud ...

Vizient

Sr AI/ML Engineer

Centennial, CO

$102.40K - $179K/yr

Design and implement RAG pipelines, embedding strategies, and vector search architectures. * Build agentic workflows, prompt strategies, and orchestration patterns for LLM systems. * Own AI/ML ...

Vizient

Sr AI/ML Engineer

Englewood, CO

$102.40K - $179K/yr

Design and implement RAG pipelines, embedding strategies, and vector search architectures. * Build agentic workflows, prompt strategies, and orchestration patterns for LLM systems. * Own AI/ML ...

Deloitte

Agentic AI, AI & Data Consultant

Denver, CO · On-site

Knowledge bases (retrieval, metadata filtering, re-ranking), Guardrails, Prompt Flows, and RAG ... Vertex AI (e.g., Model Garden, Agent Builder, custom training); Gemini API and Google AI Studio;

Vizient, Inc.

Sr AI/ML Engineer

Centennial, CO · On-site

$102.40K - $179K/yr

Design and implement RAG pipelines, embedding strategies, and vector search architectures. Build agentic workflows, prompt strategies, and orchestration patterns for LLM systems. Own AI/ML solutions ...

Eliassen Group

Senior AI/ML Engineer

Almont, CO · Remote

$90 - $100/hr

The role includes LLM orchestration, RAG pipelines, vector database integration, and multi-agent systems. The engineer will build predictive models, apply document AI with transformers, and integrate ...

Eliassen Group

Senior AI/ML Engineer

Denver, CO · Remote

$90 - $100/hr

The role includes LLM orchestration, RAG pipelines, vector database integration, and multi-agent systems. The engineer will build predictive models, apply document AI with transformers, and integrate ...

New

next page

Showing results 1-20

People also search for

All JobsAi Rag JobsAi Rag Jobs in Colorado

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 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 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 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 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

AI Solutions Architect

Ledgent Tech

Denver, CO • Hybrid

$64.75 - $85.50/hr

Other

Posted 3 days ago

Job description

Job Description
AI Solutions Architect | Enterprise AI, GenAI, and Agentic Systems
Type: Full-Time, Direct Hire
Location: Denver, CO
Schedule: Hybrid (3 days onsite)
Salary: $165k - $189k + 20% bonus
We are hiring an experienced AI Solutions Architect to lead the design, engineering, and delivery of enterprise-scale AI solutions.
This is a highly hands-on, senior role for someone who builds production AI systems end to end. You will own the architecture, code, infrastructure, and deployment of AI solutions that directly drive business outcomes.
If you have deep experience with GenAI, LLMs, RAG pipelines, and agentic systems in real production environments, this role is built for you.
What You Will Do
Build and Scale Enterprise AI Solutions

  • Design and deploy production-grade AI systems across GenAI, predictive AI, and automation
  • Own the full lifecycle from architecture through deployment and performance optimization
  • Serve as the technical authority for AI solution delivery across the organization
Lead GenAI, LLM, and RAG Development
  • Build LLM-powered applications, assistants, and multi-agent workflows
  • Develop robust RAG pipelines including ingestion, chunking, embeddings, retrieval, and reranking
  • Implement intelligent model routing based on cost, latency, and task complexity
  • Create prompt frameworks, prompt libraries, and evaluation pipelines
  • Apply guardrails for safety, accuracy, and compliance
Architect Agentic AI Systems
  • Design and build multi-agent systems with orchestration, memory, and tool usage
  • Enable agents to interact with enterprise platforms and execute multi-step workflows
  • Develop tool libraries using Python, APIs, and enterprise integrations
  • Implement intent classification, routing, and escalation logic
Own the AI Engineering Stack
  • Build and maintain AI workloads on Databricks including MLflow, model serving, and pipelines
  • Write production-grade Python across APIs, automation, and AI orchestration
  • Manage CI/CD pipelines and repository structure using GitLab
  • Deliver reliable, scalable, and observable AI systems in production
Integrate AI Into Business Systems
  • Connect AI solutions to ERPs, HR systems, collaboration tools, and project platforms
  • Build APIs, connectors, and event-driven pipelines using Azure Functions
  • Enable real-time data ingestion and AI-driven decisioning inside workflows
Drive Standards, Governance, and Reliability
  • Establish best practices across AI development, deployment, and monitoring
  • Ensure systems meet security, privacy, and compliance requirements
  • Monitor performance including latency, uptime, and cost efficiency
What You Bring
  • 7+ years in AI/ML engineering, solution architecture, or software engineering
  • Proven experience building and deploying production AI systems
Deep Technical Expertise In:
  • GenAI and LLM systems including prompt engineering and evaluation
  • RAG pipelines at scale including hybrid search, reranking, and optimization
  • Model routing strategies with fallback logic and cost optimization
  • Agentic AI frameworks such as LangChain, LlamaIndex, AutoGen or similar
  • Databricks ecosystem including MLflow and production pipelines
  • Python engineering for APIs, automation, and AI workflows
  • CI/CD and GitLab for AI solution lifecycle management
  • APIs and integrations across enterprise platforms
  • Vector databases and semantic search
What Sets You Apart
  • You have deployed RAG systems in real-world production environments
  • You have built intelligent routing systems beyond simple model switching
  • You have developed agent-based systems that execute real business workflows
  • You are comfortable owning systems end to end, from architecture to production reliability
Why This Role
  • High-impact role shaping enterprise AI strategy and execution
  • Full ownership of modern AI architecture and delivery
  • Opportunity to work on cutting-edge GenAI and agentic systems
  • Strong compensation and comprehensive benefits
  • Collaborative, fast-moving environment with real investment in AI innovation

All qualified applicants will receive consideration for employment without regard to race, color, national origin, age, ancestry, religion, sex, sexual orientation, gender identity, gender expression, marital status, disability, medical condition, genetic information, pregnancy, or military or veteran status. We consider all qualified applicants, including those with criminal histories, in a manner consistent with state and local laws, including the California Fair Chance Act, City of Los Angeles' Fair Chance Initiative for Hiring Ordinance, and Los Angeles County Fair Chance Ordinance.
Job Reference: JN -052026-422586

Apply