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Ai Rag Jobs (NOW HIRING)

DATA INDICATORS LLC

AI Architect

Dublin, OH · Remote

$64.50 - $85/hr

Architect Generative AI, LLM, RAG, semantic/vector search, AI assistant, and agentic AI solutions. * Design secure integrations between AI platforms, enterprise applications, APIs, knowledge ...

INFOTRON

GCP / AI Cloud Engineer

Sunnyvale, CA · Remote

$60K - $120K/yr

The ideal candidate will have experience building and deploying cloud-based solutions using GCP, Terraform, Vertex AI, RAG, and LLM technologies. This role is suited for an early-career cloud/AI ...

DATA INDICATORS LLC

AI Architect

Dublin, OH · Remote

$64.50 - $85/hr

Architect Generative AI, LLM, RAG, semantic/vector search, AI assistant, and agentic AI solutions. * Design secure integrations between AI platforms, enterprise applications, APIs, knowledge ...

DATA INDICATORS LLC

AI Architect

Dublin, OH · Remote

$64.50 - $85/hr

Architect Generative AI, LLM, RAG, semantic/vector search, AI assistant, and agentic AI solutions. * Design secure integrations between AI platforms, enterprise applications, APIs, knowledge ...

Tandavix llc

AI Engineer

Jersey City, NJ · On-site

Preferred Skills Agentic AI, RAG, LangChain/LangGraph, or Talk-to-Data solutions. Docker, Kubernetes, Kafka, Redis. OpenTelemetry, Grafana, Prometheus. CI/CD (Jenkins, GitHub Actions), DevOps/GitOps.

Siri InfoSolutions Inc

AI Engineer

Jersey City, NJ · On-site

Agentic AI, RAG, LangChain/LangGraph, or Talk-to-Data solutions. * Docker, Kubernetes, Kafka, Redis. * OpenTelemetry, Grafana, Prometheus. * CI/CD (Jenkins, GitHub Actions), DevOps/GitOps.

New

Digital Links Inc

AI Solution Architect

Princeton, NJ · On-site

$66 - $87/hr

Deep understanding of AI/ML concepts, including natural language processing (NLP), deep learning, generative AI, RAG architectures, and MLOps practices. Cloud Proficiency: Experience with cloud ...

Digital Links Inc

AI/ML Engineer

Minneapolis, MN · On-site

This role requires strong expertise in Generative AI, RAG (Retrieval-Augmented Generation), and enterprise integrations. The ideal candidate should be capable of independently delivering scalable AI ...

FourthSquare

Oracle Data and AI Platfrom Architect

Dallas, TX · On-site

$63.25 - $81.50/hr

Lead AI Vector Search, RAG, Generative AI, and Agentic AI solutions. * Design AI agents and integrations with Oracle Fusion ERP, HCM, SCM, and Financials . * Work with OCI Generative AI, LLMs ...

Sidram Technologies

AI DATA ENGINEER

New York, NY · On-site

$125K - $150K/yr

Generative AI / RAG / Fine-Tuning LangChain / HuggingFace Azure ML / AWS SageMaker / Google Vertex AI Neo4j / Amazon Neptune Model explainability & interpretability ML A/B testing frameworks ...

CLPS Global

Senior AI Engineer

Alpharetta, GA · On-site

$119K - $157K/yr

Agentic AI, RAG, LangChain/LangGraph, or Talk-to-Data solutions. * Docker, Kubernetes, Kafka, Redis. * OpenTelemetry, Grafana, Prometheus. * CI/CD (Jenkins, GitHub Actions), DevOps/GitOps.

Dutech Systems

We are seeking an experienced Senior Generative AI / Agentic AI Engineer to design, develop, and implement AI-powered solutions using Generative AI, Agentic AI, RAG, Custom GPTs, and workflow ...

Oscar Technology

AI/ML Engineer

Manhattan, NY · On-site

$115K - $158K/yr

Senior AI Engineer (Hybrid - NYC) Location: Greater New York City Area (3 days/week on-site ... Create and optimize prompts, embeddings, vector search, and retrieval-augmented generation (RAG ...

Unicon Systems

AI Consultant

San Jose, CA · On-site

$62 - $68/hr

Design and develop production-grade AI solutions using LLMs, AI agents, RAG pipelines, and orchestration frameworks . * Translate ambiguous business requirements into clear technical designs and ...

Unicon Systems

AI Consultant

San Jose, CA · On-site

$62 - $68/hr

Design and develop production-grade AI solutions using LLMs, AI agents, RAG pipelines, and orchestration frameworks . * Translate ambiguous business requirements into clear technical designs and ...

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Ai Rag information

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$32K

$58.2K

$83.5K

How much do ai rag jobs pay per year?

As of Sep 13, 2026, the average yearly pay for ai rag in the United States is $58,245.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $65,000.00 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.

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Ai Rag jobs near you
Infographic showing various Ai Rag job openings in the United States as of September 2026, with employment types broken down into 5% Internship, 63% Full Time, 9% Part Time, and 23% Contract. Highlights an 59% In-person, 18% Hybrid, and 23% Remote job distribution, with an average salary of $58,245 per year, or $28 per hour.

AI Architect

Dublin, OH • Remote

DATA INDICATORS LLC
IT Services • 11 - 50 employees
Learn more about DATA INDICATORS LLCexternal

$64.50 - $85/hr

Full-time

Posted 10 days ago

Job description

AI ArchitectWe are seeking a senior AI Architect to lead the design and development of enterprise AI capabilities across a healthcare and cancer research organization. This role will define the architecture, standards, and roadmap for Generative AI, machine learning, enterprise search, RAG, AI assistants, and agentic AI.
Key Responsibilities
  • Define enterprise AI architecture, standards, reference designs, and technology roadmap.
  • Architect Generative AI, LLM, RAG, semantic/vector search, AI assistant, and agentic AI solutions.
  • Design secure integrations between AI platforms, enterprise applications, APIs, knowledge repositories, and data platforms.
  • Establish reusable AI services, model-selection patterns, guardrails, evaluation, monitoring, and human-in-the-loop controls.
  • Define standards for MLOps/LLMOps, deployment, model lifecycle management, and monitoring.
  • Partner with data, cloud, security, clinical, research, and application teams to move AI solutions into production.
  • Ensure AI solutions meet requirements for security, privacy, governance, auditability, and responsible AI.
  • Provide technical leadership and communicate architecture, risks, and technology decisions to senior stakeholders.
Required Skills
  • 10+ years in enterprise architecture, solution architecture, data/cloud architecture, software engineering, AI/ML, or related disciplines.
  • Strong enterprise architecture experience with Generative AI and LLM-based platforms.
  • Strong knowledge of:
    • LLMs and Generative AI
    • RAG and enterprise search
    • Embeddings and vector databases
    • AI agents / agentic workflows
    • APIs and enterprise integrations
    • Cloud AI platforms
    • MLOps / LLMOps
  • Strong understanding of modern data architecture, cloud platforms, APIs, containers/Kubernetes, and distributed systems.
  • Experience designing solutions involving sensitive or regulated data.
  • Knowledge of AI security, privacy, governance, model risk, and responsible AI.
  • Strong technical leadership and executive communication skills.
Preferred
  • Experience with Glean or similar enterprise AI search / knowledge-management platforms.
  • Healthcare, life sciences, cancer research, pharmaceutical, or other regulated-industry experience.
  • Familiarity with FHIR, HL7, DICOM, Epic, or clinical/research data environments.
  • Experience with Azure, AWS, or Google Cloud AI platforms.
  • Experience with enterprise RAG platforms, vector databases, knowledge graphs, model gateways, or agent frameworks.
Ideal Candidate
A senior architect who combines enterprise architecture leadership with hands-on technical depth in GenAI, RAG, LLMs, agents, cloud/data architecture, and AI governance.