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Llm Ml Rag Jobs in Kansas (NOW HIRING)

Propio

AI/LLM Safety Engineer

Overland Park, KS

... ML: how models work, prompt engineering, and the safety implications of fine-tuning and RAG (e.g ... Familiarity with the LLM attack surface-prompt injection, jailbreaks, data poisoning, and supply ...

Propio

AI/LLM Safety Engineer

Leawood, KS · On-site

LLM Safety Evaluation & Red Teaming * Design and maintain a safety evaluation framework ... ML: how models work, prompt engineering, and the safety implications of fine-tuning and RAG (e.g ...

CrowdStrike

... AI/ML or Data Science at massive scale. * Demonstrable hands-on experience in LLM engineering (fine-tuning, prompt engineering, deployment), RAG, and developing agentic workflows. * Proven track ...

StackAdapt

... LLM APIs. * A solid understanding of business process analysis and re-engineering ... Solid understanding of Natural Language Processing (NLP), LLMs, and ML principles. * Experience ...

Auvik Networks

Design and build AI-powered product features, including LLM-based agents, intelligent automation ... building AI/ML-powered product features, including working with LLMs, prompt engineering, RAG ...

Kiewit

Solutions Architect II

Lenexa, KS · On-site

$59.25 - $78/hr

... workflows, RAG, and structured output Contribute to architecture decision records (ADRs) and ... NET * Demonstrated curiosity about AI/ML: prompt engineering, LLM integration, agent patterns, or ...

Kiewit Corporation

Solutions Architect II

Lenexa, KS · On-site +1

$59.25 - $78/hr

... workflows, RAG, and structured output Contribute to architecture decision records (ADRs) and ... NET * Demonstrated curiosity about AI/ML: prompt engineering, LLM integration, agent patterns, or ...

Autodesk

$159K - $285K/yr

... LLM-as-a-Judge techniques, human-in-the-loop evaluations, and quality measurement for non ... Experience working on or adjacent to AI/ML-powered products - especially those with ...

Locatee Ag

$108K - $143K/yr

About You: 5+ years in product management with shipped and validated AI or ML features in a B2B ... Fluent in LLM-specific product patterns: RAG, evaluation frameworks, prompt versioning, latency ...

Locatee AG

$108K - $143K/yr

About You: 5+ years in product management with shipped and validated AI or ML features in a B2B ... Fluent in LLM-specific product patterns: RAG, evaluation frameworks, prompt versioning, latency ...

All JobsLlm Ml Rag JobsLlm Ml Rag Jobs in Kansas

Llm Ml Rag information

What are some typical challenges faced when working on Retrieval-Augmented Generation (RAG) systems in large language model (LLM) machine learning roles?

Professionals working on LLM ML RAG systems often encounter challenges such as ensuring the accuracy and relevancy of retrieved documents, managing latency for real-time queries, and seamlessly integrating retrieval mechanisms with generation models. Additionally, keeping up with evolving datasets and maintaining high-quality knowledge bases can be demanding. Collaboration with data engineers and domain experts is common to refine retrieval pipelines and optimize the end-to-end system.

What is the difference between Llm Ml Rag vs Data Scientist?

AspectLlm Ml RagData Scientist
Required CredentialsMaster's or PhD in ML, AI, or related fields; certifications in ML frameworksDegree in Computer Science, Statistics, or related; certifications in data analysis or ML
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, product development teams
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, tech, consulting firms
Common Search & ComparisonOften compared for ML specialization and research focusCompared for data analysis, modeling, and business insights

While both roles involve working with machine learning, Llm Ml Rag typically focuses on research and development of large language models, requiring advanced ML expertise. Data Scientists often work on analyzing data, building predictive models, and deriving insights for business decisions. The roles overlap in skills but differ in focus and application areas.

What are the key skills and qualifications needed to thrive as an LLM ML RAG (Retrieval-Augmented Generation) Engineer, and why are they important?

To excel as an LLM ML RAG Engineer, you need a strong background in machine learning, natural language processing, and large language models, typically supported by a degree in computer science or a related field. Proficiency with tools and frameworks like Python, PyTorch/TensorFlow, Hugging Face Transformers, and vector databases (e.g., FAISS, Pinecone) is essential, along with experience in deploying and fine-tuning LLMs and integrating retrieval systems. Strong problem-solving skills, attention to detail, and the ability to collaborate with cross-functional teams distinguish top performers in this role. These skills ensure the effective development and deployment of advanced AI solutions that combine generative and retrieval capabilities for high-impact applications.

What are LLM ML RAG jobs?

LLM ML RAG jobs involve working with Large Language Models (LLMs), Machine Learning (ML), and Retrieval-Augmented Generation (RAG) systems. Professionals in these roles typically design, develop, and optimize AI systems that combine language models with retrieval techniques to improve accuracy, relevance, and factual grounding in generated outputs. These jobs often require expertise in natural language processing, deep learning, data engineering, and information retrieval. Key responsibilities might include integrating RAG pipelines, fine-tuning LLMs, and ensuring high-quality responses from AI applications.
What are popular job titles related to Llm Ml Rag jobs in Kansas? For Llm Ml Rag jobs in Kansas, the most frequently searched job titles are:
What job categories do people searching Llm Ml Rag jobs in Kansas look for? The top searched job categories for Llm Ml Rag jobs in Kansas are:
Llm Ml Rag jobs near you
Infographic showing various Llm Ml Rag job openings in Kansas as of July 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution.

AI/LLM Safety Engineer

Propio

Overland Park, KS

Other

Re-posted 3 days ago

Propio rating

6.1

Company rating: 6.1 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

371st of 488 rated business services

Job description

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.


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