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Llm Ml Rag Jobs in Wisconsin (NOW HIRING)

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, ...

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 ...

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 ...

AgSource

AI Engineer

Watertown, WI

... ML models. This is a strategic, hands-on position for an experienced technical leader who has a ... Architect and implement LLM agents. * Build composable, tool-augmented reasoning chains (e.g., RAG ...

Urus Group LP

AI Engineer

Watertown, WI · On-site +1

... ML models. This is a strategic, hands-on position for an experienced technical leader who has a ... Architect and implement LLM agents. * Build composable, tool-augmented reasoning chains (e.g., RAG ...

Urus

AI Engineer

Watertown, WI · On-site

... ML models. This is a strategic, hands-on position for an experienced technical leader who has a ... Architect and implement LLM agents. * Build composable, tool-augmented reasoning chains (e.g., RAG ...

Farm Systems

AI Engineer

Watertown, WI · On-site

... ML models. This is a strategic, hands-on position for an experienced technical leader who has a ... Architect and implement LLM agents. * Build composable, tool-augmented reasoning chains (e.g., RAG ...

AgSource

AI Engineer

Watertown, WI · On-site +1

... ML models. This is a strategic, hands-on position for an experienced technical leader who has a ... Architect and implement LLM agents. * Build composable, tool-augmented reasoning chains (e.g., RAG ...

Abbott

Lead AI Platform Engineer

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

Lead AI Platform Engineer

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

Lead AI Platform Engineer

Madison, WI

$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 ...

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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 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 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 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 ML/GenAI Ops Engineer - Milwaukee, WI

Harley-Davidson Motor Company

Milwaukee, WI • On-site

$102K - $141K/yr

Full-time

Re-posted 3 days ago

Job description

Job Summary:
Harley-Davidson Motor Company is a storied brand founded in 1903, known for its passion and commitment to innovation. They are seeking a Senior ML/GenAI Ops Engineer to design, develop, and operationalize machine learning and generative AI platforms, ensuring seamless integration into production environments with a focus on scalability and compliance.
Responsibilities:
• Design, develop, and maintain scalable platforms for machine learning and GenAI, supporting end-to-end processes from data ingestion to model deployment and monitoring.
• Lead end-to-end solution design for ML/AI data pipelines and model-serving platforms, ensuring architectures meet scalability, reliability, and regulatory requirements.
• Partner closely with project and program managers to establish delivery timelines, resource plans, and milestone tracking for complex, multi-team data/ML efforts.
• Champion best practices for reproducibility, automation, observability, and governance/COE in ML/AI operational pipelines and platforms.
• Oversee compute governance, alert monitoring and model lifecycle.
• Implement CI/CD pipelines for automated deployment of ML and AI models to production environments.
• Work closely with data scientists to ensure model readiness and optimization, focusing on robust deployment and monitoring.
• Develop and manage tools for continuous monitoring and performance management of models post-deployment to identify and resolve performance drift.
• Partner with data scientists, software engineers, product owners, and stakeholders to align ML and AI solutions with business goals and performance metrics.
• Facilitate seamless integration of ML/AI systems with business processes, ensuring data accessibility, quality, and real-time insights.
• Ensure systems are built for scalability, maintainability, and security, adhering to best practices in ML & AI DevOps.
• Implement monitoring solutions to proactively address any issues in data, model performance, or infrastructure.
• Drive architectural reviews, design decisions, and engineering standards that support long-term operational excellence for ML/AI workloads.
• Serve as the primary technical escalation point for delivery risks and system performance issues, ensuring timely resolution and stakeholder alignment.
• Integrate AI ethics and compliance considerations into all ML/AI solutions, with a focus on data privacy, bias detection, and model transparency.
• Implement processes to meet regulatory requirements and promote responsible AI use.
Qualifications:
Required:
• High School Diploma or Equivalent Required
• 7+ years of experience in data engineering or DevOps roles, with a focus on ML/AI platforms and infrastructure.
• Proven experience in operationalizing and automating ML and GenAI solutions in production environments.
• Strong experience with cloud platforms (AWS, Azure, GCP) and managing infrastructure for data and machine learning systems
• Proficiency in Azure Cloud Platform, specifically Azure ML Studio and Azure AI Foundry
• Proficiency in Python, SQL, and ML/AI DevOps tools (e.g., MLflow, scikit learn, PyTorch, Kubeflow, TensorFlow Extended).
• Experience with CI/CD tools (e.g., Jenkins, GitLab CI) and containerization/orchestration tools (Docker, Kubernetes).
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and data pipeline tools (e.g., Apache Airflow, dbt).
• Proficiency with vector databases, LLM workflows, or RAG pipelines.
• Familiarity with cost management, autoscaling, and GPU governance in Azure ML.
• Experience with data governance frameworks and security best practices.
• Technical Acumen: Strong knowledge of ML/AI lifecycle management, MLOps practices, and data pipeline optimization.
• Collaboration & Communication: Excellent teamwork skills with an ability to work closely with cross-functional teams and communicate complex technical concepts effectively.
• Problem-Solving: Proactive approach & proven ability to identifying and solve issues in model performance, data quality, and infrastructure bottlenecks.
• Ethics and Compliance: Deep understanding of responsible AI practices, including bias detection, explainability, and data privacy.
• Governance & Data Integrity: Ability to enforce data privacy, lineage, and data quality controls across ML workflows, ensuring compliance with enterprise and regulatory requirements.
Preferred:
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field is preferred
• Azure AZ-900 certification, with additional ML/LLM/RAG focused certifications preferred.
Company:
In 1903, out of a small shed in Milwaukee, Wisconsin, four young men lit a cultural wildfire that would grow and spread across geographies and generations. Founded in 1903, the company is headquartered in Milwaukee, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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