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Llm Ml Rag Jobs in Wisconsin (NOW HIRING)

Paradigm

WI · On-site

$120 - $150/hr

Develop and optimize CNN and LLM-powered models for computer vision, document extraction, and ... Familiarity with graph-based retrieval, RAG pipelines, or multimodal ML is preferred. #J-18808 ...

Qualysoft

Der Azure AI / ML Engineer unterstutzt Fachbereiche und IT-Teams bei der Umsetzung innovativer KI ... LLM-basierten Conversational-AI-Losungen (z. B. RAG-Applikationen) in Microsoft Azure Aufbau, ...

YDU JC Air Cond & Ref Inc.- Dubai

$85 - $127/hr

The Senior AI/ML Engineer sits at the center of this transformation, building production AI/ML and ... Implement RAG architectures, agentic workflows, and prompt‑engineering patterns for production ...

NELNET BUSINESS SOLUTIONS

AI Engineer

Madison, WI

This posting covers multiple levels within the AI / ML Engineer role. We are currently seeking ... Design, build, and deploy LLM-powered agents to solve concrete business problems. This includes ...

Xforia Global Talent & Technology Solutions

WI · On-site

... least 3 years designing AI/ML platform systems, including hands-on experience with LLM ... tool-use patterns, RAG pipelines, and agent reliability/observability Nice to Have Skills

Johnson Controls

Ai Engineer

Milwaukee, WI · On-site

Data, ML & Software Engineering * Build and maintain the data pipelines that feed AI systems ... Implement retrieval and embedding workflows (RAG, vector databases) for scalable, accurate ...

Johnson Controls

Ai Engineer

Milwaukee, WI · On-site

Data, ML & Software Engineering * Build and maintain the data pipelines that feed AI systems ... Implement retrieval and embedding workflows (RAG, vector databases) for scalable, accurate ...

Johnson Controls

AI Engineer

Glendale, WI · On-site

This AI/ML Engineer role sits at the center of that transformation. You will do two things in ... Implement RAG architectures, agentic workflows, and prompt engineering patterns for production ...

Johnson Controls

AI Engineer

Glendale, WI · On-site +1

This AI/ML Engineer role sits at the center of that transformation. You will do two things in ... Implement RAG architectures, agentic workflows, and prompt engineering patterns for production ...

Abbott

Sr. Engineer, AI Platform

Madison, WI · On-site

$99K - $198K/yr

... ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM ...

Abbott

Sr. Engineer, AI Platform

Madison, WI · On-site

$99K - $198K/yr

... ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM ...

Abbott

Sr. Engineer, AI Platform

Madison, WI · On-site

$99K - $198K/yr

... ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM ...

Jobtailor

WI · On-site

$150 - $190/hr

Develop LLM-based applications that analyze logs, telemetry, and operational data to generate ... Create Retrieval‑Augmented Generation (RAG), prompt‑engineering, and agentic AI workflows that ...

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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 Wisconsin?

For Llm Ml Rag jobs in Wisconsin, the most frequently searched job titles are:

What job categories do people searching Llm Ml Rag jobs in Wisconsin look for?

The top searched job categories for Llm Ml Rag jobs in Wisconsin are:

What cities in Wisconsin are hiring for Llm Ml Rag jobs?

Cities in Wisconsin with the most Llm Ml Rag job openings:

Llm Ml Rag jobs near you

Senior Applied ML Engineer

WI • On-site

Paradigm
See more jobs at Paradigmexternal

$120 - $150/hr

Other

Posted 21 days ago

Job description

Paradigm is a software company transforming the way that the residential, construction & building product industries operate across the globe. We are looking for a Senior Applied ML Engineer to be part of revolutionizing these industries.

We are looking for a Senior Applied ML Engineer to design, implement, and scale machine learning systems that power next-generation construction and digital twin solutions. You will apply advanced ML techniques—ranging from computer vision to large language models—to automate critical workflows such as blueprint understanding, 3D model generation, and materials forecasting. This role blends research, engineering, and domain expertise to deliver practical, production-ready AI systems that transform how homes are designed, estimated, and built.

What You Will Do
  • Develop and optimize CNN and LLM-powered models for computer vision, document extraction, and automated construction workflows.
  • Prototype, fine-tune, and assess models for NLP tasks such as classification, entity recognition, and summarization of construction data.
  • Build scalable ML pipelines and backend services that integrate into production-grade agents and digital platforms.
  • Drive the end-to-end ML lifecycle: from experimentation and training, to deployment, monitoring, and continuous improvement.
  • Integrate retrieval, ML, and rules-based methods to deliver reliable, explainable, and supportable features.
  • Collaborate closely with product managers, software engineers, and construction domain experts to solve real-world challenges with measurable business impact.
What You Need to Succeed
  • Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field.
  • 5+ years of experience designing and deploying applied ML systems at scale.
  • Experience with computer vision (CNNs, object detection, segmentation) and natural language processing (LLMs, embeddings, transformers).
  • Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
  • Experience with ML Ops platforms and deploying ML systems into production (MLflow, Kubeflow or equivalent).
  • Experience with APIs, CI/CD pipelines, cloud platforms (AWS/Azure/GCP).
  • Ability to clearly communicate technical concepts to both engineers and non-technical stakeholders.
  • Experience applying ML in construction, CAD/BIM, architecture, or digital twin platforms is preferred.
  • Familiarity with graph-based retrieval, RAG pipelines, or multimodal ML is preferred.
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