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Rag Engineer Jobs (NOW HIRING)

Gatik AI

AI Operations & RAG Architect

Mountain View, CA

$170K - $220K/yr

About the role The AI Operations Engineer is responsible for building the central knowledge base ... Secure RAG Architecture: Design and maintain the vector databases and data pipelines that power ...

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Rag Engineer information

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$59.5K

$90.5K

$153.5K

How much do rag engineer jobs pay per year?

As of Jun 28, 2026, the average yearly pay for rag engineer in the United States is $90,511.00, according to ZipRecruiter salary data. Most workers in this role earn between $68,500.00 and $105,000.00 per year, depending on experience, location, and employer.

How to become a RAG engineer?

A RAG (Red, Amber, Green) engineer typically works in risk assessment or project management, requiring a background in engineering, data analysis, or related fields. Developing skills in data visualization tools, risk management methodologies, and obtaining relevant certifications can enhance qualifications for this role.

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.

Which 3 jobs will survive AI?

For a Rag Engineer, jobs that require complex manual skills, problem-solving, and hands-on work are more likely to survive AI automation. These include roles such as skilled trades like welding or machining, specialized maintenance technicians, and quality control inspectors. Such positions often depend on physical dexterity, judgment, and adaptability that AI and automation are less capable of replicating fully.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as AI research director, senior machine learning engineer, or AI executive, often requiring advanced skills in data science, programming, and deep learning. These roles usually involve leadership, strategic planning, and extensive experience, and they may be found in large tech companies or specialized AI firms.

What engineers make $500,000?

Senior engineers in specialized fields such as petroleum, aerospace, or software engineering can earn $500,000 or more annually, especially with experience, advanced skills, and in high-demand industries. Executive engineering roles or those with significant leadership responsibilities may also reach this compensation level.
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Rag Engineer jobs near you
Infographic showing various Rag Engineer job openings in the United States as of June 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution, with an average salary of $90,511 per year, or $43.5 per hour.

Senior AI/ML Engineer AI Platform & RAG Systems

Unisoft Technology Inc

Baltimore, MD • On-site

$103K - $142K/yr

Other

Posted yesterday

Job description

Job Summary

Seeking a Senior AI/ML Engineer with strong experience in building scalable AI platforms, retrieval-augmented generation (RAG) systems, and production-grade ML/data pipelines for enterprise environments. The ideal candidate will have deep expertise in AI/ML engineering, cloud-native architecture, data engineering, and deploying secure, scalable solutions into production.

Key Responsibilities

  • Design and build multi-tenant AI platforms, including agentic workflows, RAG services, and LLM orchestration.
  • Develop LLM-powered applications for intelligent automation, enterprise search, and knowledge retrieval.
  • Implement and optimize vector search and retrieval pipelines using OpenSearch kNN, metadata indexing, and hybrid search.
  • Build secure, event-driven ingestion pipelines integrating data lakes, streaming systems, and document processing workflows.
  • Design advanced chunking and document parsing strategies to improve retrieval relevance across multiple file types.
  • Develop LLM evaluation pipelines, golden datasets, custom evaluators, and explainable scoring mechanisms.
  • Implement feedback and human-in-the-loop systems to improve AI performance in production.
  • Establish observability for AI systems, including tracing, latency monitoring, token usage, and model performance insights.
  • Build and optimize batch and real-time data pipelines for ML and analytics workloads.
  • Implement MLOps practices for model training, deployment, versioning, and monitoring.
  • Ensure security, governance, compliance, and responsible AI controls across enterprise deployments.
  • Partner with architecture, product, and security teams to define readiness criteria and production rollout plans.

Required Qualifications

  • 10+ years of overall IT experience with strong focus on AI/ML engineering, data engineering, or platform engineering.
  • Strong hands-on programming experience in Python and SQL.
  • Proven experience building RAG systems, LLM-based applications, and AI orchestration workflows.
  • Strong knowledge of vector databases or vector search technologies such as OpenSearch kNN or similar platforms.
  • Experience building ETL/ELT and ML-ready data pipelines using Spark, PySpark, or similar big data frameworks.
  • Hands-on experience with streaming technologies such as Kafka, Kinesis, or Event Hub.
  • Experience with MLOps tools and deployment frameworks such as MLflow, Docker, Kubernetes, and CI/CD pipelines.
  • Strong experience with AWS and/or Azure cloud ecosystems.
  • Experience implementing observability, monitoring, and evaluation frameworks for AI systems.
  • Knowledge of secure enterprise architecture including RBAC, OAuth2, PII handling, and compliance controls.
  • Bachelor s or Master s degree in Computer Science, Engineering, or a related field.

Preferred Qualifications

  • Experience with enterprise AI platforms such as C3.ai, AWS AI, or Azure AI services.
  • Familiarity with agentic AI, multi-agent systems, and tool-based LLM workflows.
  • Experience with Delta Lake, Snowflake, OpenSearch, and modern cloud data platforms.
  • Exposure to regulated industries such as banking, healthcare, or financial services.
  • Experience with Terraform, ArgoCD, autoscaling frameworks, and cloud-native infrastructure.
  • Ability to translate business requirements into scalable, production-ready AI solutions.

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