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Rag Engineer Jobs in Arizona (NOW HIRING)

OmegaHires

Lead Gen AI Engineer

Phoenix, AZ · On-site

$101K - $134K/yr

We are looking for a Lead Gen AI Engineer with strong expertise in Python, Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI to lead the design and development of enterprise AI ...

OmegaHires

Gen AI Engineer with Python

Phoenix, AZ · On-site

Gen AI Engineer with Python We are looking for a Gen AI Engineer with strong expertise in Python, Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI to lead the design and ...

Lorven Technologies

AI Engineering Leader

Tempe, AZ · On-site

$98K - $129K/yr

This role i not a pure management role - the ideal candidate will actively design, build, and scale AI systems (RAG, agents, evaluation frameworks) while leading engineering initiatives and ...

Perficientcorp Inc

Gen AI Engineer

Phoenix, AZ · On-site

Job Title - Gen AI Engineer Location - Phoenix, AZ Duration: 12+ Months Interview Mode - In-Person Interview Tech Stack - AI / Agentic AI, LLM, RAG, FastAPI, GCP, Flask, Python, SQL, Docker ...

Inficare Technologies

AI Python Engineer

Phoenix, AZ · On-site

Build and enhance AI/ML and GenAI-powered solutions using Python, LLMs, RAG, prompt engineering, and agentic AI frameworks * Develop models for NLP, classification, clustering, anomaly detection, and ...

Synergie Systems

Junior AI Engineer

Phoenix, AZ · On-site

Junior AI Engineer Location: Phoenix, AZ Job Type: Only W2 · Develop and deploy AI/ML models for ... LLMs. · Develop RAG (Retrieval-Augmented Generation) pipelines using embeddings and vector ...

OmegaHires

Lead Gen AI Engineer with Python

Phoenix, AZ · On-site

$139K - $170K/yr

Lead Gen AI Engineer with Python We are looking for a Lead Gen AI Engineer with strong expertise in Python, Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI to lead the design and ...

Inficare Technologies

Data Engineer

Phoenix, AZ · On-site

$113K - $136K/yr

Job Title: Data Engineer Location: Phoenix, AZ (Day 1 Onsite) Job Type: Contract - Long Term ... Support AI/ML and GenAI teams with data requirements, vector databases, embeddings, and RAG ...

Judge Group, Inc.

Software Engineer

Phoenix, AZ · On-site

$49 - $53/hr

Software Engineer, AI-Powered Data Platforms (Google Cloud) We are not accepting C2C or 1099 ... Develop agent-based solutions using modern AI frameworks, Retrieval-Augmented Generation (RAG ...

ClifyX

AI Prompt Engineer

Chandler, AZ · On-site

... RAG) using vector search and enterprise knowledge sources. • Continuously optimize prompts for ... of prompt engineering frameworks and design patterns. • Hands-on experience with RAG ...

Confiz LLC

Principal AI Engineer

Phoenix, AZ · On-site

Strong understanding of LLMs, RAG architectures, agentic AI systems, prompt engineering, model fine-tuning, and MLOps. * Databricks: Hands-on experience with Databricks Lakehouse (Delta Lake, Unity ...

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All JobsRag Engineer JobsRag Engineer Jobs in Arizona

Rag Engineer information

See Arizona salary details

$55.4K

$84.3K

$143K

How much do rag engineer jobs pay per year?

As of Aug 19, 2026, the average yearly pay for rag engineer in Arizona is $84,346.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,800.00 and $97,800.00 per year, depending on experience, location, and employer.

What is the difference between Rag Engineer vs Textile Technician?

AspectRag EngineerTextile Technician
Required CredentialsEngineering degree, technical certificationsDiploma or degree in textiles or related field
Work EnvironmentFactories, manufacturing plants, R&D labsTextile mills, production facilities, quality control labs
Industry UsageDesigning and improving rag production processesMonitoring textile quality, testing fabrics

While both roles involve working within the textile industry, a Rag Engineer primarily focuses on the engineering aspects of rag production, process optimization, and machinery, whereas a Textile Technician concentrates on fabric testing, quality control, and ensuring textile standards are met. The roles often overlap in industry settings but differ in technical focus and responsibilities.

How to become a rag engineer?

To become a rag engineer, you typically need a bachelor's degree in engineering, materials science, or a related field. Relevant skills include knowledge of manufacturing processes, quality control, and proficiency with industry tools and equipment; certifications in quality management or safety can also be beneficial. Gaining experience through internships or entry-level positions in manufacturing environments is important for career advancement.

What are popular job titles related to Rag Engineer jobs in Arizona?

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

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

The top searched job categories for Rag Engineer jobs in Arizona are:

What cities in Arizona are hiring for Rag Engineer jobs?

Cities in Arizona with the most Rag Engineer job openings:

Rag Engineer jobs near you
Infographic showing various Rag Engineer job openings in Arizona as of August 2026, with employment types broken down into 92% Full Time, 3% Part Time, 2% Temporary, and 3% Contract. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution, with an average salary of $84,346 per year, or $40.6 per hour.

AI Engineer

Ruri Software Technologies LLC

Scottsdale, AZ • On-site

Full-time

Posted 20 days ago

Job description

Job Title :AI Engineer
Location:Hybrid work schedule at client location (9501 E. Shea Blvd., MC 081, Scottsdale, AZ 85260)
Job Description:
Required Skills & Experience:
• 5+ years of Strong Software Engineering (Python/NodeJS), system design and production service experience.
• 2+ years of Experience with LLMs, prompt engineering, and agent frameworks.
• 2+ years of Experience Practical experience implementing RAG: embeddings, vector DBs and retrieval tuning.
• 2+ years of Experience with Lang Chain patterns and with toolchain telemetry (Langfuse or similar) for prompt/model traceability.
• 5+ years of Experience with Kubernetes, Docker, CI/CD and infrastructure as code experience.
• 2+ years of Experience with Practical experience with Google Cloud Platform services
• 2+ years of Experience with Observability, testing, and security best practices for distributed systems.
• 2+ years of Experience with evaluating and mitigating retrieval/augmentation failures, hallucinations, and leakage risks in RAG systems.
• Familiarity with vendor and open source vector stores and embedding providers.
• Familiarity with CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, or ArgoCD).
About the Role:
We are seeking an experienced AIML Engineer to design, build, and operate AI/ML infrastructure and agentic systems. This role involves developing MCP servers and agents, integrating LLMs, and implementing RAG pipelines for production environments.
Key Responsibilities:
• Design, build and operate MCP servers and MCP agents that host, orchestrate and monitor AI/agent workloads.
• Develop agentic AI, prompt engineering patterns, LLM integrations and developer tooling for production use.
• Own deployment, scaling, reliability and cost-efficiency on Kubernetes/Docker and Google Cloud with automated CI/CD
• Design and implement RAG (Retrieval Augmented Generation) pipelines and integrations with vector stores and retrieval tooling; use LangChain and Langfuse for orchestration, chaining, and observability.
Core Responsibilities:
• Implement and maintain MCP server and agent code, APIs, and SDKs for model access and agent orchestration.
• Design agent behavior, workflows and safety guards for agentic AI systems.
• Create, test and iterate prompt templates, evaluation harnesses and grounding/chain of thought strategies.
• Integrate LLMs and model providers (self hosted and cloud APIs) with unified adapters and telemetry.
• Build developer tooling: CLI, local runner, simulators, and debugging tools for agents and prompts.
• Containerize services (Docker), manage orchestration (Kubernetes/GKE), and optimize nodes, autoscaling and resource requests.
• Ensure observability: logging, metrics, traces, dashboards, alerting and SLOs for model infra and agents.
• Create runbooks, playbooks and incident response procedures; reduce MTTR and perform postmortems.
• Design and maintain RAG workflows: document chunking, embeddings, vector indexing, retrieval strategies, re ranking and context injection.
• Integrate and instrument LangChain for composable chains, agents and tooling; use Langfuse (or equivalent tracing) to capture prompts, model calls, RAG traces and evaluation telemetry.
Years of Experience: 5 Years of Experience
Regards
Surya
Surya@rurisoft.com

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