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Llm Ml Rag Jobs in Kansas (NOW HIRING)

Factualiq

Machine Learning Engineer

Mission, KS · On-site

Build production-grade LLM and RAG-based tools for retrieval, reasoning, and inference that AI agents can call as part of automated workflows. * Create robust APIs and SDKs that expose ML models as ...

Propio

AI/LLM Safety Engineer

Overland Park, KS · On-site

... 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

Overland Park, 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 ...

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Llm Ml Rag information

What is an llm ml rag job?

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 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 are the key skills and qualifications needed to thrive as an llm ml rag 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 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 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 August 2026, with employment types broken down into 91% Full Time, 3% Part Time, and 6% Contract. Highlights an 79% Physical, 6% Hybrid, and 15% Remote job distribution.

Machine Learning Engineer

Mission, KS • On-site

Factualiq
See more jobs at Factualiqexternal

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Key responsibilities

  • Build ML/AI systems and tools that power FactualIQ's Decision Engine for enterprise clients.

  • Own the complete ML lifecycle, including feature engineering, model development, experimentation, deployment, and performance optimization.

  • Develop production-grade ML models, APIs, and services, including synthetic data pipelines, model versioning, monitoring, and observability systems.

Job description

Build the ML/AI systems powering FactualIQ’s enterprise Decision Engine. Design scalable AI tools, deploy production-grade models, and enable intelligent agent workflows that drive mission-critical decision-making for enterprise clients.

As a Machine Learning Engineer, you’ll build ML/AI tools that power FactualIQ’s Decision Engine for our enterprise clients. You’ll own the complete development lifecycle, from synthetic data generation and model development to deploying production APIs and services that autonomous agents consume. Your work will include simulation pipelines, forecasting tools, RAG systems, and inference services that enable decision intelligence at scale.

This is a hands-on technical role where you’ll work alongside senior engineers and cross-functional teams to build reliable, performant ML systems that support client-critical AI decision-making for Fortune 1000 clients, while growing your expertise in advanced ML engineering for enterprise AI applications.

What You’ll Do
  • Design and deploy ML/AI tools and services that power FactualIQ’s Decision Engine, a multi-agent workflow platform that integrates ML and LLM capabilities for enterprise decision-making.
  • Own the full ML lifecycle: feature engineering, model development, experimentation, A/B testing, deployment, and performance optimization at scale.
  • Build production-grade LLM and RAG-based tools for retrieval, reasoning, and inference that AI agents can call as part of automated workflows.
  • Create robust APIs and SDKs that expose ML models as reusable, production-grade services with clear contracts, error handling, and observability.
  • Develop synthetic data generation pipelines to create training datasets, accelerate model iteration, and enable rapid customization for client-specific use cases.
  • Implement model versioning, experiment tracking, and rollback procedures to ensure reproducibility and safe iteration across production deployments.
  • Build monitoring and observability systems for deployed models, including performance degradation detection, drift monitoring, and automated alerting.
  • Develop and maintain documentation for ML services, APIs, model architectures, and operational runbooks to support cross-team collaboration.
  • Collaborate with platform and agent teams to understand requirements, define tool interfaces, and ensure ML services integrate seamlessly into engine workflows.
  • Participate in sprint planning, technical design reviews, and team knowledge-sharing to contribute to FactualIQ’s engineering culture and delivery cadence.
  • Stay current with emerging best practices in ML engineering and AI systems, incorporating learnings into your work.
What You’ll Bring
  • Bachelor’s or Master’s degree in Computer Science, Data Science, or related technical field (or equivalent practical experience).
  • 5+ years of experience building and deploying ML models in production environments, with recent hands-on experience in LLM or GenAI systems.
  • Expert-level Python programming with deep knowledge of ML frameworks (PyTorch, TensorFlow, or similar) and performance considerations for production workloads.
  • Production experience with cloud ML platforms (AWS, GCP, or Azure) and MLOps tools (MLflow, Kubeflow, or similar) including model deployment, monitoring, and lifecycle management.
  • Experience with modern LLM and retrieval-based systems including RAG architectures, vector databases, and embeddings, with understanding of chunking strategies, context optimization, and retrieval quality evaluation.
  • Experience developing synthetic data generation pipelines or data simulation systems for ML training and evaluation.
  • Demonstrated ability to build and deploy scalable model APIs and production ML infrastructure, including versioning, error handling, and observability.
  • Proficiency with SQL and data pipeline tools, including feature engineering workflows and understanding of data modeling trade-offs for ML use cases.
  • Familiarity with CI/CD practices, containerization (Docker), and version control (Git).
  • Experience working in multi-tenant or enterprise environments with awareness of data isolation, security requirements, and compliance considerations.
  • Strong communication skills and ability to collaborate effectively with cross-functional engineering teams and stakeholders.
What You Might Bring
  • Expertise in forecasting, time series, network analytics, optimization, or reinforcement learning.
  • Experience with LLM orchestration frameworks (LangChain, LlamaIndex, DSPy), prompt engineering strategies, or multi-modal LLM applications.
  • Experience with advanced vector database optimization, hybrid search strategies, or embedding model customization.
  • Experience with distributed model training, model optimization at scale, or high-performance production ML systems.
  • Knowledge of bias detection, hallucination mitigation, prompt injection defenses, or compliance frameworks for enterprise AI.
  • Experience mentoring junior engineers or contributing to team knowledge-sharing and technical onboarding.
  • Contributions to open-source projects, technical blog posts, or community involvement in ML engineering topics.
  • Cloud ML certification (AWS ML Specialty, GCP Professional ML Engineer, or similar) or published ML research.
What WE VALUE
  • A growth mindset, building on the recognition that a good engineer is always learning.
  • Creative, entrepreneurial flexibility to try innovative approaches to solving problems, coupled with the resilience to recognize mistakes quickly, adapt and correct course as needed to achieve success.
  • Speed to solutions, with rapid, well-planned iterations.
  • Design-forward approaches to building technology products, coupled with a test-heavy technique to ensure that both the problem to be solved and the solution context are clear and optimal before development begins.
  • Transparent, frequent and constructive communication skills and practices.
  • Low-ego collaboration, where feedback is valued, everyone’s voice is heard, debates and disagreements are used for the team’s benefit, and commitment matters.
  • Mission alignment and care for delivering highest-standard quality to support our clients’ success.
Reporting

The role currently reports to FactualIQ’s Chief Strategy Officer, with a dotted line to a Senior Machine Learning Engineer. As we build out our engineering team, the role will ultimately report to a Tech Lead or Senior Tech Lead.

Career progression from this role will lead to a Senior Machine Learning Engineer position.

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