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Retrieval Augmented Generation Jobs in Utah (NOW HIRING)

ConsultNet

Senior Data Architect

Salt Lake City, UT · On-site

$90 - $100/hr

... as Retrieval-Augmented Generation (RAG), machine learning workflows, vector data, and modern AI architectures • Understanding of how data architecture enables Large Language Models (LLMs), AI ...

Packsize International LLC

Sr. AI Engineer

Salt Lake City, UT · On-site

$53.50 - $69/hr

Retrieval-Augmented Generation (RAG) * Multi-agent orchestration * Enterprise integrations (SAP, Salesforce, Databricks, SharePoint, Azure Ecosystem) Guide use-case prioritization and platform ...

Packsize

Sr. AI Engineer

Salt Lake City, UT · On-site

$101K - $138K/yr

Retrieval-Augmented Generation (RAG) * Multi-agent orchestration * Enterprise integrations (SAP, Salesforce, Databricks, SharePoint, Azure Ecosystem) Guide use-case prioritization and ...

Packsize

Sr. AI Engineer

Salt Lake City, UT

$101K - $138K/yr

Retrieval-Augmented Generation (RAG) * Multi-agent orchestration * Enterprise integrations (SAP, Salesforce, Databricks, SharePoint, Azure Ecosystem) Guide use-case prioritization and ...

Kforce Technology Staffing

AI Engineer

Draper, UT · On-site

Develop and productionize LLM-based solutions, including prompt engineering, retrieval-augmented generation (RAG) pipelines, fine-tuning, and multimodal models * Build and orchestrate agentic AI ...

Epicor

Manager of Product Development | AI Platform

Lehi, UT · Hybrid

$107K - $134K/yr

Knowledge of modern artificial intelligence frameworks including retrieval-augmented generation, vector databases, orchestration frameworks, and observability tools * Familiarity with emerging ...

Corporation Service Company

Software Engineer

Logan, UT · On-site

AI/ML background: intelligent document processing (IDP), OCR, NLP, RAG (retrieval-augmented generation), vector databases, or ML model training and deployment (MLOps) . * Cloud experience ( Azure ...

eNett

Senior Backend Engineer - AI Platform

Salt Lake City, UT · On-site +1

$118K - $156K/yr

Design solutions for context management, memory, and retrieval-augmented generation (RAG) to enhance agent effectiveness. Experience you'll bring: * Bachelor's degree in Computer Science or Software ...

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Retrieval Augmented Generation information

What are the typical daily responsibilities of a Retrieval Augmented Generation engineer?

A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.

What is a Retrieval Augmented Generation job?

A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.

What are the key skills and qualifications needed to thrive in the Retrieval Augmented Generation position, and why are they important?

To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.

What are the most commonly searched types of Retrieval Augmented Generation jobs in Utah? The most popular types of Retrieval Augmented Generation jobs in Utah are:
What are popular job titles related to Retrieval Augmented Generation jobs in Utah? For Retrieval Augmented Generation jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Retrieval Augmented Generation jobs in Utah look for? The top searched job categories for Retrieval Augmented Generation jobs in Utah are:
What cities in Utah are hiring for Retrieval Augmented Generation jobs? Cities in Utah with the most Retrieval Augmented Generation job openings:
Retrieval Augmented Generation jobs near you
Infographic showing various Retrieval Augmented Generation job openings in Utah as of June 2026, with employment types broken down into 36% Full Time, and 64% Part Time. Highlights an 66% Physical, 2% Hybrid, and 32% Remote job distribution.
Applied AI Field Engineer (Orlando)

Applied AI Field Engineer (Orlando)

Trilon Group

Salt Lake City, UT

$155K - $190K/yr

Full-time

Posted 21 days ago

Job description

Description
Trilon is building a supercharged, technology-enabled future for our people and partners. The Applied AI Engineer plays a critical role in that mission by building the AI-powered features that enable our tools to compress real engineering labor across our operating companies. 
This role sits at the intersection of software engineering and applied AI, focused on designing and implementing the intelligence layer of our products. You translate product requirements and architectural patterns into working AI capabilities by building prompt frameworks, retrieval-augmented generation pipelines, and agent-based workflows that operate against real engineering data and deliverables. 
Working within a product pod, you partner closely with the Lead Engineer, Software Engineer, and QA Engineer to deliver production-ready solutions. You own how the system reasons, including prompt design, context management, model integration, and orchestration logic. You also help define how quality is measured for AI outputs, ensuring tools are accurate, reliable, and usable in real-world workflows. 
You will engage directly with engineers across our operating companies to understand workflows, validate solutions, and iterate quickly based on feedback. You may also participate in field-based project hackathons, embedding with teams to identify high-impact opportunities and rapidly prototype solutions that inform platform development. 
This role requires strong software engineering fundamentals, deep hands-on experience with modern AI tooling, and the ability to operate in a fast-moving environment where both the technology and the product are evolving. You are comfortable with ambiguity, rigorous about output quality, and focused on delivering AI that engineers trust and use. 

Key Responsibilities
AI Application Development
  • Design and build AI-powered features using large language models and related tooling 
  • Develop and maintain prompt architectures that drive consistent, high-quality outputs 
  • Implement retrieval-augmented generation pipelines using enterprise data sources 
  • Build and orchestrate agent-based workflows to automate targeted tasks 
Model Integration and System Behavior 
  • Integrate LLM APIs such as Anthropic Claude and OpenAI into production systems 
  • Design context management strategies to ensure outputs are grounded, relevant, and accurate 
  • Manage tradeoffs across latency, cost, and performance in AI workflows 
  • Continuously improve system behavior through prompt iteration and architecture refinement
Pod Collaboration and Delivery 
  • Partner with Software Engineers to integrate AI capabilities into applications, APIs, and user interfaces 
  • Align with the Lead Engineer on technical direction, architecture, and implementation decisions 
  • Work with QA Engineers to define evaluation criteria, testing strategies, and quality thresholds for AI outputs 
  • Translate product requirements into scalable, production-ready AI solutions 
Evaluation and Quality Optimization 
  • Define and implement approaches for evaluating non-deterministic AI outputs 
  • Build test cases, benchmarks, and evaluation pipelines to track output quality over time 
  • Identify failure modes and iterate on prompts, pipelines, and orchestration logic 
  • Ensure consistency and reliability as models, prompts, and data sources evolve 
Continuous Improvement and Innovation 
  • Stay current with advancements in LLMs, vector databases, and agent frameworks 
  • Experiment with new tools and techniques to improve speed, quality, and capability 
  • Contribute reusable patterns, components, and best practices across pods 


Skills, Knowledge and Expertise
  • 4+ years of experience in software engineering, applied AI, or machine learning development 
  • Strong programming skills in Python and/or JavaScript 
  • Hands-on experience working with LLM APIs such as Anthropic Claude, OpenAI, or similar 
  • Experience designing and implementing prompt architectures and prompt engineering techniques 
  • Experience building retrieval-augmented generation pipelines and working with vector databases 
  • Familiarity with agent orchestration frameworks and multi-step AI workflows 
  • Experience integrating AI capabilities into applications via APIs and backend systems 
  • Strong understanding of handling structured and unstructured data in AI systems 
  • Ability to evaluate, debug, and improve non-deterministic AI outputs 
  • Experience working in a fast-paced, product-oriented development environment 
  • Strong problem-solving skills and ability to operate in ambiguous, evolving contexts 
  • Ability to collaborate closely with engineers, product managers, and QA within a pod structure 
  • Excellent communication skills and ability to explain technical concepts clearly 
  • Curiosity and willingness to learn domain-specific workflows, particularly within engineering and AEC contexts