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Rag Engineer Jobs in Kansas (NOW HIRING)

CrowdStrike

Partner closely with Product, Engineering, Security, Infrastructure, and Operations teams to ... LLMs, RAG, Agentic AI, Multi-Agent Systems, AI Evaluation * Vector Search, Hybrid Search ...

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Robots and Pencils

Robots & Pencils is an applied AI engineering firm building the next frontier of business ... Deep understanding of AI concepts including LLMs, RAG, copilots, agents, evaluation frameworks, and ...

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All JobsRag Engineer JobsRag Engineer Jobs in Kansas

Rag Engineer information

See Kansas salary details

$53.1K

$80.7K

$136.9K

How much do rag engineer jobs pay per year?

As of Sep 5, 2026, the average yearly pay for rag engineer in Kansas is $80,722.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,100.00 and $93,600.00 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 Kansas?

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

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

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

What cities in Kansas are hiring for Rag Engineer jobs?

Cities in Kansas with the most Rag Engineer job openings:

Rag Engineer jobs near you
Infographic showing various Rag Engineer job openings in Kansas as of August 2026, with employment types broken down into 90% Full Time, 5% Part Time, 1% Temporary, and 4% Contract. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution, with an average salary of $80,722 per year, or $38.8 per hour.

AI/LLM Safety Engineer

Propio Language Services

Overland Park, KS • On-site, Remote

Full-time

Re-posted 12 days ago

Key responsibilities

  • Design and maintain safety evaluation frameworks, including adversarial prompt sets and test suites, to validate models and agents before deployment.

  • Build, iterate, and implement guardrail logic and safety checks into runtime and CI/CD pipelines to prevent unsafe behavior.

  • Perform threat modeling for agent scenarios, conduct safety reviews of RL environments and data, and collaborate with teams to embed safety constraints.

Job description

Job Type
Full-time
Description
We are seeking an AI/LLM Safety Engineer to join our AI team and take ownership of how safely our models and agents behave in production; with a focus on AI Safety, Trust & Safety, and Responsible AI. You will design the evaluations that catch unsafe behavior, build the guardrails that stop it, and lead the red-teaming that finds the gaps before our users-or attackers-do. Agent safety is the primary focus of this role: you will help ensure that as our systems gain the ability to call tools and take actions, they do so within well-defined, well-tested boundaries.
Key Responsibilities:
LLM Safety Evaluation & Red Teaming
  • Design and maintain a safety evaluation framework-adversarial prompt sets, scenario-based test suites, and regression suites-so that every model and agent update is validated before it ships.
  • Lead structured red-teaming exercises covering jailbreaks, prompt injection, tool misuse, and data exfiltration; document findings and drive each issue through to remediation and closure.

Guardrails & Runtime Controls
  • Build and iterate on guardrail logic, including input/output filtering, tool-boundary constraints, action validation, sensitive-data redaction, and policy prompting.
  • Integrate safety checks into CI/CD and runtime so that unsafe behavior is intercepted before it reaches users.

Agent Safety (primary focus of this role)
  • Perform threat modeling for agentic scenarios: tool-call boundaries, sandbox isolation, and least-privilege access, with particular attention to preventing agents from exfiltrating data or executing irreversible actions through chained tool calls.
  • Conduct safety reviews of reinforcement-learning (RL) environments and trajectory data, partnering with environment and agent engineering teams to embed safety constraints directly into the environments themselves.

Monitoring & Observability
  • Instrument AI features for safety with structured logging, tracing, and metrics, enabling detection of unsafe patterns and regressions in production.

Governance & Collaboration
  • Prepare evidence for governance reviews-test reports, evaluation summaries, and mitigation validation-aligned with internal Responsible AI standards.
  • Collaborate with Product and UX to improve safety interactions (warnings, confirmations, refusal messaging, and feedback collection), and align evaluation goals with the Research and Data teams.

Requirements
  • Bachelor's or Master's degree in Computer Science, Software Engineering, Cybersecurity, or a related technical field-or equivalent practical experience.
  • 4+ years building production software, with direct experience working on-or securing-ML/LLM systems.
  • Strong software engineering skills with the ability to write production-grade code (primarily Python), beyond scripting or notebook prototyping.
  • Solid understanding of LLMs and ML: how models work, prompt engineering, and the safety implications of fine-tuning and RAG (e.g., unsafe retrieval, tool misuse, and data exfiltration).
  • A security mindset with demonstrated threat-modeling ability; able to threat-model AI workflows and familiar with the fundamentals of access control, data retention, and incident response.
  • Familiarity with the LLM attack surface-prompt injection, jailbreaks, data poisoning, and supply-chain risk-and working knowledge of the OWASP LLM Top 10.
  • Hands-on experience with at least one of safety evaluation or red teaming, with the ability to walk through a real finding and how it was remediated.

Preferred Qualifications
  • Hands-on experience with industry safety tooling such as garak, PyRIT, promptfoo, Giskard, and NeMo Guardrails, and the ability to articulate the trade-offs between them.
  • Visible output in AI safety or security: publications at relevant venues (e.g., the NeurIPS AI Safety Workshop, USENIX Security, or DEF CON AI Village), open-source contributions, or responsible disclosures on frontier models with public write-ups.
  • Familiarity with AI governance and compliance frameworks (NIST AI RMF, ISO/IEC 42001, EU AI Act) and the ability to translate compliance requirements into concrete engineering tasks.
  • Engineering experience with agents, RL environments, and/or tool use.
  • Practical experience with threat-modeling methodologies such as MITRE ATLAS and STRIDE/PASTA.

About Propio
Propio is on a mission to make communication accessible to everyone. As a leader in real-time interpretation and multilingual language services, we connect people with the information they need across language, culture, and modality. We are committed to building AI-powered tools that enhance interpreter workflows, automate multilingual insights, and scale communication quality across industries.
Notice of AI Use in Job Application Review
As part of our commitment in creating a fair, efficient, and consistent hiring process we may use artificial intelligence (AI) to help our recruiting teams organize, summarize, and analyze information provided by candidates, including resumes, application responses, and other materials submitted during the application process.AI may be used to identify patterns, highlight relevant skills, and experience, and assist in comparing a candidate's qualifications with the requirement of a specific role. These tools are to improve efficiency and consistency while supporting more informed hiring decisions, which will ultimately be made by the hiring team.

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