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Llm Ml Rag Jobs in Chicago, IL (NOW HIRING)

Veracity

AI/ML Engineering Senior Advisor

Deerfield, IL · On-site

$138.80K - $139.30K/yr

LLM / GenAI experience: building, fine-tuning, or prompting models such as GPT-4, LLaMA, Claude ... Familiarity with RAG (Retrieval-Augmented Generation) pipelines and integration into enterprise ...

Tata Consultancy Service Limited

AI Engineer

Chicago, IL · On-site

$100K - $120K/yr

Key Responsibilities • Design, develop, and deploy AI/ML and Generative AI solutions including LLM based applications, RAG pipelines, agents, and predictive models • Translate business use cases ...

Tata Consultancy Service Limited

AI Engineer

Chicago, IL · On-site

$100K - $120K/yr

Key Responsibilities • Design, develop, and deploy AI/ML and Generative AI solutions including LLM based applications, RAG pipelines, agents, and predictive models • Translate business use cases ...

Cercle Partnership

ML/AI Engineer

Chicago, IL · Remote

This isn't just another LLM wrapper - we're pioneering graph-based AI architectures that transform ... ● Build cutting-edge RAG systems combining vector databases, knowledge graphs, and ...

Deloitte

Agentic Delivery Engineer

Chicago, IL · On-site

... RAG) approaches, and early-stage AI prototyping under the guidance of more senior team members • Contribute to the integration of AI/ML models, including generative AI and LLM-based solutions, into ...

Pulsora, Inc.

AI Engineer - United States

Chicago, IL · Remote

$50K - $60K/yr

... and RAG-based systems. What you will do * LLM Integration & Development : Design, develop, and ... AI/ML development, particularly with LLMs * Strong proficiency in Python and its relevant data ...

Allstate

AI Engineer Lead

Chicago, IL · On-site

$105.60K - $139.10K/yr

... AI/ML solutions. * Experience with GenAI technologies and techniques (e.g., RAG, fine-tuning ... Java/ReactJS (optional) 2) LLM & GenAI Engineering * Hands-on experience with LLMs (preferably GPT ...

Allstate

AI Engineer Lead

Chicago, IL

$105.70K - $139.20K/yr

... AI/ML solutions. * Experience with GenAI technologies and techniques (e.g., RAG, fine-tuning ... Java/ReactJS (optional) 2) LLM & GenAI Engineering * Hands-on experience with LLMs (preferably GPT ...

Deloitte

AI Data Engineer - Senior Consultant

Chicago, IL · Hybrid

$107.60K - $147.80K/yr

... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

Deloitte

AI Engineer Senior Consultant

Chicago, IL · Hybrid

$107.60K - $147.80K/yr

... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

iLink Digital

Senior Graph AI Engineer

North Chicago, IL · On-site

$117.80K - $155.30K/yr

Integrating graph data with AI/ML pipelines * LLM integration (RAG with knowledge graphs) * Prompt engineering and context orchestration * Building GenAI apps using LangChain / LlamaIndex * Fine ...

Whiztek Corp

GenAI engineer

Chicago, IL · On-site

... RAG pipelines, managing vector data at scale, and engineering MCP servers to standardize LLM ... Expert-level proficiency in Python (specifically for AI/ML application development). * GenAI ...

iLink Digital

Senior Graph AI Engineer

North Chicago, IL · On-site

$117.80K - $155.30K/yr

Integrating graph data with AI/ML pipelines * LLM integration (RAG with knowledge graphs) * Prompt engineering and context orchestration * Building GenAI apps using LangChain / LlamaIndex * Fine ...

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Llm Ml Rag information

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$46.4K

$77.6K

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How much do llm ml rag jobs pay per year?

As of May 28, 2026, the average yearly pay for llm ml rag in Chicago, IL is $77,569.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,900.00 and $89,600.00 per year, depending on experience, location, and employer.

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 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 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 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 Chicago, IL? For Llm Ml Rag jobs in Chicago, IL, the most frequently searched job titles are:
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Llm Ml Rag jobs near you
AI/ML Engineering Senior Advisor

AI/ML Engineering Senior Advisor

Veracity

Deerfield, IL • On-site

$138.80K - $139.30K/yr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Job Title: AI/ML Engineering Senior Advisor
Job Location: Deerfield, IL (Hybrid - 3 days / week onsite)
# Positions: 1
Employment Type: C2H
Duration: Long Term
Key Technology: Python, TensorFlow, PyTorch, Keras, OpenCV, DVC, MLflow, Git, CI/CD
Job Responsibilities: Work on ML Solution for RxQuality for MFC Project
Skills and Experience Required:
  • 7+ years of hands-on experience in applied machine learning, deep learning, and AI system deployment
  • Strong Python engineering background with ML/DL frameworks: TensorFlow, PyTorch, Keras, OpenCV
  • Proven experience in Computer Vision tasks, including object detection, segmentation, and OCR
  • Experience training and fine-tuning models such as: YOLOv5/v8, EfficientNet, Faster-RCNN, TrOCR, Vision Transformers (ViT)
  • Practical experience building and serving REST APIs for inference (TF Serving, TorchServe, FastAPI)
  • Hands-on with MLOps tools: DVC, MLflow, Git, CI/CD, containerization (Docker/Kubernetes)
  • Cloud deployment experience (Azure preferred; AWS or GCP acceptable)
  • LLM/GenAI experience: building, fine-tuning, or prompting models such as GPT-4, LLaMA, Claude, etc.
  • Familiarity with RAG (Retrieval-Augmented Generation) pipelines and integration into enterprise systems
  • Understanding of Agentic AI architectures (e.g., LangChain, CrewAI, AutoGPT) for orchestrated task agents or workflow automation
  • Strong foundations in statistics, optimization, and deep learning principles
  • Clear understanding of AI governance, fairness, and model explainability.

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