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Ai Rag Jobs in Cary, NC (NOW HIRING)

Gallagher

Sr. AI Engineering Lead

Raleigh, NC · On-site

NET and Python) for RAG pipelines, AI agent orchestration, and API integrations; remain a strong individual contributor while leading the team. * Set Engineering Standards: Establish patterns for ...

ACA Group

AI Operations Engineer

Durham, NC · On-site

$67K - $90K/yr

About the Role We are seeking an AI Operations Engineer to help support and optimize our next ... Help improve retrieval quality for vector search and RAG pipelines * Assist with evaluation ...

ACA Group

AI Operations Engineer

Durham, NC · Hybrid

$67K - $90K/yr

About the Role We are seeking an AI Operations Engineer to help support and optimize our next ... Help improve retrieval quality for vector search and RAG pipelines * Assist with evaluation ...

First Citizens Bank

Principal AI Architect

Raleigh, NC · On-site

Define and own enterprise AI and GenAI reference architectures, including LLM platforms, RAG patterns, agentic systems, and multimodal solutions. * Establish standardized architectural patterns for ...

First Citizens Bank

Principal AI Architect

Raleigh, NC · On-site

Define and own enterprise AI and GenAI reference architectures, including LLM platforms, RAG patterns, agentic systems, and multimodal solutions. * Establish standardized architectural patterns for ...

First Citizens Bank

Principal AI Architect

Raleigh, NC · On-site

Define and own enterprise AI and GenAI reference architectures, including LLM platforms, RAG patterns, agentic systems, and multimodal solutions. * Establish standardized architectural patterns for ...

Northern Base

AI Solution Lead

Cary, NC · On-site

... RAG), embeddings, vector databases, prompt engineering, and context engineering ✔ Experience packaging, deploying, serving, and monitoring AI/ML models for real-time and batch inference ✔ Hands ...

Material Bank

Applied AI Engineer

Raleigh, NC · On-site

Develop and optimize Retrieval-Augmented Generation (RAG) systems, including embeddings, vector search, retrieval pipelines, chunking strategies, and relevance tuning. * Build multimodal AI workflows ...

Deloitte

Google AI Architect

Raleigh, NC · On-site

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 ...

RELX

Responsible AI Governance Specialist

Raleigh, NC · On-site

$15.75 - $21/hr

Document explainability and transparency practices, including Agentic AI and RAG architecture, Agentic RAG workflows, source citations, Shepard's validation, reasoning workflows, and grounding in ...

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All JobsAi Rag JobsAi Rag Jobs in Cary, NC

Ai Rag information

See Cary, NC salary details

$29.6K

$54K

$77.4K

How much do ai rag jobs pay per year?

As of Sep 4, 2026, the average yearly pay for ai rag in Cary, NC is $53,959.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,400.00 and $60,200.00 per year, depending on experience, location, and employer.

What is an AI RAG?

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 are the key skills and qualifications needed to thrive as an AI researcher?

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 common challenges faced by AI RAG 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 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 Cary, NC?

For Ai Rag jobs in Cary, NC, the most frequently searched job titles are:

What job categories do people searching Ai Rag jobs in Cary, NC look for?

The top searched job categories for Ai Rag jobs in Cary, NC are:

What cities near Cary, NC are hiring for Ai Rag jobs?

Cities near Cary, NC with the most Ai Rag job openings:

Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Cary, NC as of August 2026, with employment types broken down into 77% Full Time, 19% Part Time, and 4% Contract. Highlights an 71% Physical, 4% Hybrid, and 25% Remote job distribution, with an average salary of $53,959 per year, or $25.9 per hour.

Sr. AI Engineering Lead

Gallagher

Raleigh, NC • On-site

Full-time

Re-posted 21 days ago

Arthur J. Gallagher & Co. rating

7.6

Company rating: 7.6 out of 10

Based on 93 frontline employees who took The Breakroom Quiz

218th of 315 rated insurance

Job description

Introduction
Welcome to Gallagher - a global community of people who bring bold ideas, deep expertise, and a shared commitment to doing what’s right. We help clients navigate complexity with confidence by empowering businesses, communities, and individuals to thrive. At Gallagher, you’ll find more than a job; you’ll find a culture built on trust, driven by collaboration, and sustained by the belief that we’re better together. Whether you join us in a client-facing role or as part of our brokerage division, our benefits and HR consulting division, or our corporate team, you’ll have the opportunity to grow your career, make an impact, and be part of something bigger. Experience a workplace where you’re encouraged to be yourself, supported to succeed, and inspired to keep learning. That’s what it means to live The Gallagher Way.

Overview

The Senior AI Engineering Lead owns the technical delivery of enterprise AI solutions at AJ Gallagher. Reporting to the Director of AI, you will lead the design, build, and deployment of production AI systems - RAG pipelines, agentic workflows, and Copilot integrations - on our Azure and .NET/Python stack. You will set engineering standards for a growing AI team, mentor developers, and partner directly with business units to turn ambiguous problems into shipped, governed, production-grade AI products in a regulated insurance environment.


How you'll make an impact
  • Translate Business Problems into Technical Solutions: Work side-by-side with business leaders and subject-matter experts to unpack ambiguous, unstructured challenges and shape them into well-scoped, buildable AI solutions with a clear delivery path.
  • Own AI Solution Architecture: Design and defend end-to-end architectures for GenAI and agentic systems on Microsoft Foundry - spanning Azure OpenAI, Azure AI Search, Foundry Tools, and Copilot Studio - that meet enterprise security, scalability, and data-residency requirements.
  • Lead Hands-On Delivery: Write and review production code (C#/.NET and Python) for RAG pipelines, AI agent orchestration, and API integrations; remain a strong individual contributor while leading the team.
  • Set Engineering Standards: Establish patterns for agentic development, evaluation, testing, CI/CD (Azure DevOps), and observability so AI solutions are repeatable and maintainable - not one-off prototypes.
  • Mentor and Grow Engineers: Coach AI developers through design reviews, pairing, and career development; raise the technical bar across the team.
  • Ship to Enterprise Surfaces: Deliver AI capabilities into internal applications, Microsoft Teams, and M365 Copilot, integrating through Foundry agents, Service Bus, Azure Functions, and other cloud-native services.
  • Build Governance In: Implement responsible-AI controls - Entra ID/RBAC, IaC Policy, human-in-the-loop checkpoints, audit logging - aligned to Gallagher compliance expectations from day one, not as an afterthought.
  • Drive Evaluation and Reliability: Define measurable quality bars (groundedness, accuracy, latency, cost) and build the evaluation harnesses to enforce them before and after release.
  • Communicate Up and Across: Translate technical trade-offs into clear recommendations for the Director of AI and business stakeholders; manage delivery risks and dependencies proactively.

About You
  • Experience: 7 or more years in software engineering, including experience in building LLM/GenAI systems in production and 2+ years leading engineers or owning technical direction.
  • Azure Depth: Hands-on production experience with Microsoft Foundry, Azure OpenAI Service, Azure AI Search, and Azure compute (Functions, Container Apps, or App Service).
  • Languages: Strong .NET/C# plus working proficiency in Python; able to review and contribute in both.
  • GenAI Engineering: Proven delivery of RAG architectures, agent orchestration (Microsoft Agent Framework, Semantic Kernel, or LangChain), and prompt/evaluation pipelines.
  • API and Integration Skills: Designing secure REST APIs and event-driven integrations (Azure API Management, Service Bus) within an enterprise identity model (Entra ID, RBAC).
  • Delivery Discipline: Agile experience with Azure DevOps (or equivalent) - backlogs, CI/CD, automated testing, and release management.
  • Leadership: Track record of mentoring engineers, running design reviews, and owning technical decisions across multiple concurrent initiatives.
Preferred Differentiators
  • Regulated Industry Experience: Prior AI/ML delivery in insurance, financial services, or healthcare, with familiarity with model risk management or responsible AI guidance.
  • Familiarity with AI-enabled and spec-driven development: Using AI-assisted tooling and agent workflows across the SDLC to quickly deliver production-grade systems.
  • Experience with Infrastructure-as-Code: Building applications tightly aligned to Terraform-deployed, optimized infrastructure.
  • Evaluation and MLOps Maturity: Built LLMOps tooling - automated evals, red-teaming, drift/cost monitoring - at enterprise scale.
  • Architecture Credentials: Azure certifications (AZ-305, AI-103) or equivalent demonstrated architecture ownership.
Professional Qualities
  • Bias for Shipped Outcomes: Measures success by working software in users' hands, not demos or decks; cuts scope intelligently to ship.
  • Calm Technical Authority: Makes and defends decisions under ambiguity, and changes course quickly when evidence demands it.
  • Force Multiplier: Gets more from the team than from their own keyboard - through mentoring, standards, and unblocking others.
  • Governance as a Feature: Treats compliance, security, and auditability as design inputs that build trust, not friction to route around.
  • Clear Communicator: Explains complex systems simply to executives and precisely to engineers; writes things down.
  • Pragmatic Curiosity: Tracks the fast-moving AI landscape but adopts new tools only when they solve a real AJ Gallagher problem.

Compensation and benefits

At Gallagher, we believe supporting our colleagues goes far beyond the role itself. For more information, visit our Benefits page.

  • Competitive compensation
  • Comprehensive benefits programs designed to support your well-being 
  • Career development opportunities and ongoing learning 
  • A collaborative, people-first culture with accessible leadership 
  • The opportunity to do meaningful work with global reach and local impact 

At Gallagher, we are dedicated to building an inclusive and authentic workplace. If your past experience doesn’t align perfectly, we encourage you to join our Talent Community to stay connected to additional career opportunities. At times, we will consider transferable skills from previous roles.

Gallagher is an affirmative action/equal opportunity employer (Minorities/Females/Veterans/Disabled)

Qualifications:
  • Experience: 7 or more years in software engineering, including experience in building LLM/GenAI systems in production and 2+ years leading engineers or owning technical direction.
  • Azure Depth: Hands-on production experience with Microsoft Foundry, Azure OpenAI Service, Azure AI Search, and Azure compute (Functions, Container Apps, or App Service).
  • Languages: Strong .NET/C# plus working proficiency in Python; able to review and contribute in both.
  • GenAI Engineering: Proven delivery of RAG architectures, agent orchestration (Microsoft Agent Framework, Semantic Kernel, or LangChain), and prompt/evaluation pipelines.
  • API and Integration Skills: Designing secure REST APIs and event-driven integrations (Azure API Management, Service Bus) within an enterprise identity model (Entra ID, RBAC).
  • Delivery Discipline: Agile experience with Azure DevOps (or equivalent) - backlogs, CI/CD, automated testing, and release management.
  • Leadership: Track record of mentoring engineers, running design reviews, and owning technical decisions across multiple concurrent initiatives.
Preferred Differentiators
  • Regulated Industry Experience: Prior AI/ML delivery in insurance, financial services, or healthcare, with familiarity with model risk management or responsible AI guidance.
  • Familiarity with AI-enabled and spec-driven development: Using AI-assisted tooling and agent workflows across the SDLC to quickly deliver production-grade systems.
  • Experience with Infrastructure-as-Code: Building applications tightly aligned to Terraform-deployed, optimized infrastructure.
  • Evaluation and MLOps Maturity: Built LLMOps tooling - automated evals, red-teaming, drift/cost monitoring - at enterprise scale.
  • Architecture Credentials: Azure certifications (AZ-305, AI-103) or equivalent demonstrated architecture ownership.
Professional Qualities
  • Bias for Shipped Outcomes: Measures success by working software in users' hands, not demos or decks; cuts scope intelligently to ship.
  • Calm Technical Authority: Makes and defends decisions under ambiguity, and changes course quickly when evidence demands it.
  • Force Multiplier: Gets more from the team than from their own keyboard - through mentoring, standards, and unblocking others.
  • Governance as a Feature: Treats compliance, security, and auditability as design inputs that build trust, not friction to route around.
  • Clear Communicator: Explains complex systems simply to executives and precisely to engineers; writes things down.
  • Pragmatic Curiosity: Tracks the fast-moving AI landscape but adopts new tools only when they solve a real AJ Gallagher problem.
Education:UNAVAILABLEEmployment Type: FULL_TIME

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