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Rag Jobs in Colorado (NOW HIRING)

AppFolio

CO · On-site

Who We Are Looking For We're hiring a Staff Machine Learning Engineer to help move forward the ML platform that every AI initiative at AppFolio depends on -- training, fine-tuning, inference, RAG ...

Vizient, Inc.

Sr AI/ML Engineer

Centennial, CO · On-site

$102K - $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 ...

AppFolio

CO

Who We Are Looking For We're hiring a Staff Machine Learning Engineer to help move forward the ML platform that every AI initiative at AppFolio depends on -- training, fine-tuning, inference, RAG ...

BillGO

Senior AI Data Analytics Engineer

Fort Collins, CO · On-site

$132.80 - $196.50/hr

As the architect of the data models, semantic layers, and AI/RAG patterns that Product, Finance, Risk, and Operations all build on, this person turns scattered payments data into a single source of ...

TIFIN

AI Engineering, TIFIN.ai

Boulder, CO · On-site

$128K - $168K/yr

Build agent workflows that integrate RAG-based retrieval, agent memory, and knowledge graphs for grounded, long-horizon reasoning, fine-tuning models where prompting and retrieval alone fall short

BillGO, Inc.

AI Data Analytics Engineer

Fort Collins, CO · On-site

$102K - $146K/yr

Integration of large language models, embeddings, and retrieval-augmented generation (RAG) systems that turn analytics data into intelligent experiences. * Data pipelines, evaluation frameworks, and ...

BillGO, Inc.

AI Data Analytics Engineer

Fort Collins, CO

$113K - $135K/yr

Integration of large language models, embeddings, and retrieval-augmented generation (RAG) systems that turn analytics data into intelligent experiences. * Data pipelines, evaluation frameworks, and ...

Palnar

ONLY W2 | AI Engineer@ Denver, Co

Denver, CO · On-site

Solid understanding of LLM architectures, embeddings, and retrieval-augmented generation (RAG). * Proficiency in Python, JavaScript/TypeScript , or similar programming languages. * Experience with ...

AppFolio

CO · On-site

$17.50 - $20.50/hr

RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. * AI safety & authorization: Hands-on experience operating AI guardrails ...

Themesoft Inc

REMOTE_Senior Gen AI Engineer

Denver, CO · On-site

$120 - $190/hr

Developing and maintaining LLM-powered solutions| including prompt engineering| agent orchestration| tool calling| and Retrieval-Augmented Generation (RAG) implementations. * Creating and supporting ...

AppFolio

CO · On-site

$17.50 - $20.50/hr

RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. * AI safety & authorization: Hands-on experience operating AI guardrails ...

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Showing results 41-60

All JobsRag JobsRag Jobs in Colorado

Rag information

See Colorado salary details

$42.6K

$82.8K

$124.6K

How much do rag jobs pay per year?

As of Sep 2, 2026, the average yearly pay for rag in Colorado is $82,810.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,100.00 and $98,300.00 per year, depending on experience, location, and employer.

What is a RAG?

RAG stands for Retrieval-Augmented Generation, a model architecture that combines information retrieval with generative AI. In this role, a RAG specialist or engineer works on designing, implementing, and optimizing systems that retrieve relevant data from large databases to provide more accurate and informed AI-generated responses. This position typically requires strong knowledge of natural language processing, information retrieval, and deep learning frameworks. RAG models are particularly useful in applications like customer support, search engines, and knowledge management systems.

What skills and qualifications are needed to thrive as a RAG engineer?

To thrive as a Retrieval-Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and software engineering, often with a degree in computer science or a related field. Familiarity with frameworks like PyTorch or TensorFlow, experience with vector databases, and knowledge of APIs for language models are typically required. Problem-solving, effective communication, and adaptability are crucial soft skills for collaborating with teams and navigating evolving technologies. These skills are important to successfully develop, deploy, and maintain RAG systems that enhance the performance and relevance of AI-driven applications.

What are common challenges faced by RAG engineers when integrating retrieval systems with large language models?

RAG engineers often encounter challenges in ensuring the seamless integration of retrieval systems with large language models, such as maintaining low latency while fetching relevant documents and ensuring retrieved data is contextually appropriate for generation tasks. Balancing retrieval accuracy and computational efficiency is key, especially when dealing with large-scale or real-time applications. Effective collaboration with data engineers, NLP researchers, and product teams is essential to continuously refine retrieval pipelines and improve the relevance of generated outputs.

What is the difference between Rag vs Data Analyst?

AspectRagData Analyst
Required CredentialsVaries, often no formal degreeBachelor's degree in data-related field, often certifications
Work EnvironmentFieldwork, on-site, or warehouse settingsOffice-based, computer-focused
Employer & Industry UsageConstruction, manufacturing, logisticsFinance, marketing, healthcare, tech
Common Search & ComparisonRag vs Data AnalystData Analyst roles and responsibilities

While Rags typically work in physical environments handling materials or equipment, Data Analysts focus on interpreting data to inform business decisions. Both roles require analytical skills but differ significantly in credentials, work setting, and industry applications.

What are the most commonly searched types of Rag jobs in Colorado?

The most popular types of Rag jobs in Colorado are:

What are popular job titles related to Rag jobs in Colorado?

For Rag jobs in Colorado, the most frequently searched job titles are:

What job categories do people searching Rag jobs in Colorado look for?

The top searched job categories for Rag jobs in Colorado are:

Rag jobs near you
Infographic showing various Rag job openings in Colorado as of August 2026, with employment types broken down into 89% Full Time, 7% Part Time, and 4% Contract. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution, with an average salary of $82,810 per year, or $39.8 per hour.

Staff Machine Learning Engineer

AppFolio

CO • On-site

Full-time

Re-posted 3 days ago

AppFolio rating

7.2

Company rating: 7.2 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

185th of 247 rated software companies

Job description

Hi, We're AppFolio
We're innovators, changemakers, and collaborators. We're more than just a software company — we're building the AI-native platform where the real estate industry comes to do business. We're transforming Property Management; how property managers operate, how residents live, and how intelligence flows across an entire industry.
Realm-X is AppFolio's AI-native platform powering this transformation. It enables a new generation of intelligent capabilities across our products, including Realm-X Assistant (copilot), Flows (AI Agentic workflows) and Performers (autonomous AI Agents). Realm-X serves as both a foundation for internal teams to build and scale AI-powered products, and a core layer delivering intelligent, high-impact experiences directly to our customers.
At its core, Realm-X is built on a structured domain ontology and a set of shared business primitives—such as transactions, actions, reports, metrics, and skills—that enable AI systems to deeply understand and operate across the full context of property management workflows. This foundation allows us to build context-aware, action-oriented AI systems that go beyond simple assistance to power real automation and decision-making.
Who We Are Looking For
We're hiring a Staff Machine Learning Engineer to help move forward the ML platform that every AI initiative at AppFolio depends on — training, fine-tuning, inference, RAG, evaluation, and cost. You'll keep our AI cloud always-on, observable, and economical, while staying close enough to applications to influence model and agent design.
This role works at the intersection of ML infrastructure, applied AI, and cost discipline. You'll partner closely with our Voice & Agents and Research ML engineers to harden their prototypes into production systems, and help move forward the platform layer that lets Realm-X scale across AppFolio's entire customer base.
Your Impact
  • ML Platform: Design and operate AppFolio's ML infrastructure on AWS — ECS, SageMaker, GPU fleets, model serving, autoscaling, and cost controls.
  • Drive AI Cost Discipline: Optimize cost across all AI applications — provider routing, caching, batch vs. real-time, model size selection, and inference economics.
  • Multi-Provider Reliability: Maintain reliable, multi-provider LLM access across Google, OpenAI, and Anthropic with sensible fallbacks and abstractions.
  • Training & Fine-Tuning Stack: Build the training and fine-tuning stack for Small Language Models, including data pipelines, GPU orchestration, and evaluation.
  • Productionize Research: Partner with Voice & Agents and Research ML engineers to harden their prototypes into production systems with SLOs, on-call rotations, and observability.
  • AI Safety & Guardrails: Operate AppFolio's AI safety and authorization layer — guardrails on AWS, scoped tool permissions, and human-in-the-loop gates for autonomous agent actions.
Qualifications
  • Systems thinker: You think in terms of platforms and long-term leverage, not just features.
  • Production builder: You've built and scaled ML infrastructure in production with meaningful business impact.
  • Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction.
  • Owner-operator: You take ownership with a founder/owner-operator mindset, act with urgency, and focus on outcomes.
  • Pace: You have a strong desire to move fast and deliver impact, while maintaining sound engineering judgment.
  • Collaboration: You are humble, collaborative, and low-ego, and you elevate those around you.
  • Sustainability: You value work-life balance as a foundation for sustained high performance.
  • Reliability mindset: You treat ML infra like any other production system — SLOs, on-call, observability, postmortems.
Must Have
  • ML infra at scale: Has built and operated production ML infrastructure on AWS — ECS, SageMaker, GPUs, autoscaling, and cost controls.
  • Inference platforms: Production experience with model serving for both LLMs and custom models; understands quantization, batching, and routing.
  • Provider breadth: Direct experience integrating with Google (Vertex / Gemini), OpenAI, and Anthropic APIs in production.
  • Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference.
  • Cloud-native engineering: Strong Python, Docker, dependency management, and CI/CD for AI workloads.
  • RAG & agents: Working knowledge of LangChain / LangGraph and modern RAG patterns over structured and unstructured data.
  • Cost optimization: Demonstrated experience reducing unit cost of AI workloads without regressing quality or latency.
  • AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems.
Nice to Have
  • Experience training Small Language Models for production use.
  • GPU performance tuning (vLLM, TensorRT, Triton, or similar).
  • Prior Staff-level role at a company with a significant AI infra footprint.
  • Experience with ontology-driven systems or knowledge graphs supporting AI applications.
  • Contributions to open-source ML infrastructure or LLM tooling.
Location
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