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Ai Rag Jobs in Colorado (NOW HIRING)

Ombud

Senior Full Stack AI Engineer

Denver, CO · On-site

... agentic AI platform. The work splits across two domains: building the agentic engine itself ... RAG and CAG architecture: embeddings, retrieval, re-ranking, caching strategies. We use LlamaIndex ...

R Systems

AI Solution Architect

Denver, CO · On-site

$64.75 - $85.50/hr

... RAG - No less than 4 years • Proven experience architecting and delivering end-to-end AI solutions in cloud environments Azure, AWS, GCP • Able to define the right technical direction ...

Deloitte

Google AI Lead Architect

Denver, CO · On-site

$56.75 - $78/hr

Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability. * Define end-to-end architectures across data ...

Snowflake

Sr. Enterprise Data & AI Architect

Denver, CO · On-site

$69.25 - $92.75/hr

... RAG/agentic patterns for training, deployment, and monitoring at scale. • Advise on build vs. buy decisions and cost models for training, inference, and managed AI services. • Develop a deep ...

Sagard

AI Builder / Forward Deployed Engineer

Denver, CO · On-site

$90K/yr

Build and deploy production-ready AI applications, including RAG pipelines, agentic workflows, and LLM integrations, with a pragmatic eye toward what actually works at scale. * Ensure solutions meet ...

Hiring Dreams LLC

AI Python Sr. Developer

Denver, CO · On-site

$125K - $168K/yr

Build intelligent workflows leveraging LLMs, RAG, tool calling, and multi-agent systems. * Cloud Deployment & Operations (25%) - Deploy, monitor, and optimize AI applications and agents on Google ...

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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.

Which AI is best at RAG?

For an AI Rag role, the best AI systems for Retrieval-Augmented Generation (RAG) tasks typically include models like OpenAI's GPT-4, Google's Bard, and Meta's Llama 2, which are capable of integrating retrieval components with language generation. Success in RAG depends on the model's ability to efficiently access and incorporate external data, as well as the implementation of effective retrieval mechanisms and fine-tuning. Skills in natural language processing, knowledge of retrieval systems, and experience with relevant tools are essential for this role.

What engineer makes 500,000 a year?

Senior software engineers, especially those working in high-demand fields like artificial intelligence or machine learning at large tech companies, can earn $500,000 or more annually. Compensation often includes base salary, bonuses, and stock options, and requires advanced skills, extensive experience, and often a master's or Ph.D. in a related field.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer, AI research director, or executive roles like AI CTO. These roles often require advanced skills in data science, deep learning, and experience with tools like TensorFlow or PyTorch, along with a strong track record of innovation and leadership in the field.

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.

Which 3 jobs will survive AI?

AI Rag is a role that involves managing and interpreting AI outputs, and jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to survive AI automation. Examples include healthcare professionals, skilled tradespeople, and roles in education. These jobs often require human judgment, interpersonal skills, and adaptability that AI cannot fully replicate.

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 July 2026, with employment types broken down into 81% Full Time, 6% Part Time, and 13% Contract. Highlights an 100% In-person job distribution.
Senior Full Stack AI Engineer

Senior Full Stack AI Engineer

Ombud

Denver, CO • On-site

Full-time

Re-posted 16 days ago

Job description

  • Location: Denver, CO (hybrid - Tue/Wed/Thu in office)
  • Reports to: CEO
The role
We're hiring a senior full-stack engineer to build the next generation of Ombud's agentic AI platform. The work splits across two domains: building the agentic engine itself (LlamaIndex / agent orchestration / tool calling / evaluation pipelines) and the ML data engineering that supports it (embeddings, vector store operations, retrieval quality, RAG/CAG architectures).
These are high-output IC roles. You will ship production code, own systems end-to-end, and operate without a layer of engineering management between you and the product direction. You will work directly with the CEO on architectural decisions and directly with the platform engineer on production deployment. We are not hiring engineering managers and we are not hiring junior engineers.
What you'll own
  • Ombuddy Native: our next-generation agentic platform replacing the existing Chrome extension. Production agent orchestration, tool design, multi-step reasoning workflows.
  • RAG and CAG architecture: embeddings, retrieval, re-ranking, caching strategies. We use LlamaIndex and Qdrant today; we will evolve the stack as needed.
  • LLM integration with Anthropic Claude (primary), with multi-model routing where it makes sense.
  • Evaluation pipelines: how we measure response quality, regression-test prompts, and ship LLM-dependent features with confidence.
  • Self-service infrastructure: customer onboarding flows, content ingestion automation, in-product setup experiences.
  • Full-stack feature delivery across Python (primary backend), Node.js (legacy services), React (frontend), PostgreSQL, and Elasticsearch.
  • Production operations: own your features through deployment, monitoring, and customer-facing incidents.
  • Code review and technical mentorship within a small, senior engineering team.
Must-haves
  • 6+ years of professional full-stack software engineering experience, with demonstrated production system ownership.
  • Deep Python fluency. JavaScript / TypeScript / React competence.
  • Hands-on production experience integrating LLMs into product (Anthropic, OpenAI, Google) - not academic, not prototype work, but features that customers use.
  • Working knowledge of RAG architectures, embeddings, vector databases, and the trade-offs between retrieval and context-caching approaches.
  • Fluency with Claude Code or similar AI-augmented development workflows. We expect our engineers to use AI as a force multiplier on their own output.
  • Strong intuition for system design: can take a vague product goal, design the architecture, and ship the implementation without needing intermediate hand-holding.
  • Comfort operating in a small team without a layer of engineering management. You bring problems with proposed solutions, not just problems.
  • Willingness to be in-office Tuesday through Thursday in Denver.
Nice-to-haves
  • Production experience with LlamaIndex, LangChain, LangGraph, or similar agent orchestration frameworks.
  • Experience designing and operating evaluation pipelines for LLM applications (Langfuse, Braintrust, or custom).
  • Vector database operations at scale (Qdrant, Pinecone, Weaviate).
  • Browser extension or Office add-in development (Chrome extensions, Office365 / Excel add-ins).
  • Open source contributions, particularly in the AI tooling ecosystem.
  • Prior experience in revenue operations, sales enablement, or response management software.
What success looks like
First 30 days
  • Ship your first production pull request within the first two weeks.
  • Develop a working mental model of the codebase across the agentic engine, frontend, and platform layers.
  • Take ownership of one feature in flight.
First 60 days
  • Own a feature end-to-end: design, implementation, deployment, observability.
  • Contribute meaningfully to an architectural decision (engine choice, retrieval strategy, eval design).
  • Be on-call rotation capable.
First 90 days
  • Drive a substantive piece of the Ombuddy Native or self-service roadmap.
  • Establish yourself as a trusted technical voice on architectural decisions.
  • Ship measurable improvements to either response quality, system performance, or developer velocity.
Why Ombud
This is the engineering team that builds the actual product behind the agentic enterprise era. We use Claude as a teammate, not a feature checkbox. We deploy frequently, ship real customer value, and trust our engineers to operate as senior partners - not as cogs in a sprint. If you've been waiting for an environment where your AI fluency translates directly to product impact, this is it.
ABOUT OMBUD
Ombud is a Denver-based B2B SaaS company building the agentic AI platform that powers Revenue Operations teams at enterprises like Workday, UKG, and Prudential. Our product, Ombuddy, automates the response work - RFPs, security questionnaires, proposals - that has historically eaten enterprise sales cycles. Our 2026 strategy is to extend this from response management into Orchestrated Revenue Operations: autonomous execution of the discrete sales processes that move revenue. Our 2035 BHAG is $1B ARR powering 80% of discrete B2B sales motions.
We run on EOS. We hire for output, not pedigree. We expect honesty over politeness, decisions over discussions, and execution over enthusiasm.
HOW WE WORK - PIRCC VALUES
  • Progressive - We grow. We learn. We push the model forward, not protect the status quo.
  • Integrity - We do the right thing and keep our commitments. Said and done are the same thing.
  • Resourceful - We turn constraints into creativity. We do more with less and bring solutions, not problems.
  • Customer-Centric - Our customers' success is the metric that matters. We anticipate their needs and earn their trust.
  • Community - We build a team people want to be part of, and we invest in the communities we serve.

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