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Llm Ml Rag Jobs (NOW HIRING)

StackAI

Forward Deployed AI Engineer

New York, NY · On-site

$153K - $200K/yr

Expertise in AI/ML, RAG pipelines, LLM workflows, and enterprise data analytics. * Eagerness to build a business in a fast-paced environment. * Ability to travel 10-20% of the time. About StackAI ...

Siemens Healthineers

AI LLM Engineer

Atlanta, GA · On-site +1

Python (required) with experience in LLM frameworks and ML libraries * Deep understanding of RAG pipelines, Prompt Engineering and evaluation, Agent based architectures * Experience with Azure Open ...

Guidewire Software

Software Engineer III (AI/ML)

$113K - $199K/yr

Are you experienced in designing agentic workflows and integrating state-of-the-art LLM, ML, and ... as RAG, ReAct, and agent-based reasoning. * Experience building or integrating MCP servers to ...

Prophecy Technologies

AI ML Engineer

Sunrise, FL · On-site

The role focuses on LLM-based architectures, agentic workflows, RAG pipelines, and end-to-end model ... Design and implement AI/ML solutions from POC to production * Translate business requirements into ...

Natera

Senior AI/ML Engineer

$107K - $146K/yr

Role Overview The Senior AI/ML Engineer is responsible for designing, building, and deploying ... Stand up LLM runtimes with token/rate governance, caching, and safe tool-use * Implement RAG at ...

Cresta

Senior Machine Learning Engineer

$125K - $165K/yr

... RAG) systems in enterprise environments. * Experience building and evaluating complex agentic or multi-step LLM workflows. * Strong knowledge of modern ML frameworks and tools (e.g., PyTorch ...

Staffingine LLC

AI/ML Tech lead

Nashville, TN · On-site

Job Title: AI/ML Tech lead Job Location: Nashville, TN Job Type: Contract * Design and implement ... Apply LLM and RAG architectures to solve realworld problems * Integrate MS OpenAI services and ...

Cresta

OR · On-site

This track requires strong pre-LLM ML foundations, deep expertise in LLMs and modern prompting ... Design and evolve multi-agent orchestration frameworks that combine RAG, structured knowledge ...

Cresta

Senior Machine Learning Engineer

OR · On-site +1

$205K - $270K/yr

... RAG) systems in enterprise environments. * Experience building and evaluating complex agentic or multi-step LLM workflows. * Strong knowledge of modern ML frameworks and tools (e.g., PyTorch ...

Cresta

Staff Machine Learning Engineer

This track requires strong pre-LLM ML foundations, deep expertise in LLMs and modern prompting ... Design and evolve multi-agent orchestration frameworks that combine RAG, structured knowledge ...

Vertiv Co

AI / ML Platform Engineer

Westerville, OH

Demonstrated experience with prompt engineering , RAG pipeline development, and LLM API integration ... AWS AI/ML certifications (AWS Certified Machine Learning - Specialty, or AWS Certified AI ...

Carter Support Services

AI/ML Engineer

Burbank, CA · On-site

$111K - $153K/yr

Develop and optimize prompt engineering strategies for LLM-based systems * Build and deploy RAG ... ML roles * 7+ years of Python experience (expert-level proficiency required) * 7+ years of ...

Soni Resources

Senior AI/ML Engineer

New York, NY · On-site

$210K/yr

Design, develop, and maintain production-grade AI and ML solutions across cloud and hybrid ... Experience integrating LLM applications with enterprise data sources and retrieval systems (RAG)

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Llm Ml Rag information

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$45K

$75.3K

$110K

How much do llm ml rag jobs pay per year?

As of Jun 26, 2026, the average yearly pay for llm ml rag in the United States is $75,300.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,000.00 and $87,000.00 per year, depending on experience, location, and employer.

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.
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Llm Ml Rag jobs near you
Infographic showing various Llm Ml Rag job openings in the United States as of June 2026, with employment types broken down into 97% Full Time, 1% Part Time, and 2% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution, with an average salary of $75,300 per year, or $36.2 per hour.

Sr. AI/ML Engineer (LLM)

Bomnin Automotive Group

Miami, FL

Other

Posted 17 days ago

Job description

Job description Company Description Dealer Automation Technologies is a leading Information Technology Services company that provides Software as a Service (SaaS) solutions for the automotive industry. The company specializes in Process Automation, Advanced Analytics, and Augmented Intelligence to redefine dealership operations. With a focus on intuitive user experiences and cutting-edge technologies, Dealer Automation Technologies is paving the way for innovation in the sector.

Combining the agility of a startup with the expertise and business acumen of seasoned leaders, the company operates without dependence on legacy systems, fostering a dynamic and innovative work culture. Role Description This is a full-time, on-site role located in Miami, FL, for a Senior AI/ML Engineer specializing in Large Language Models (LLMs) to join our team. You will play a key role in designing and implementing workflows that leverage large language models (LLMs, LAMs, LMMs, LVLMs, etc.) to automate processes and drive innovation in our products

The ideal candidate will have a deep understanding of NLP, experience with foundational models, and a flexible, problem-solving mindset. You will collaborate closely with cross-functional teams, contributing to the development of scalable AI driven solutions. Other primary responsibilities include designing and implementing machine learning models, particularly in natural language processing and large language models, building scalable algorithms, conducting research on neural networks, and evaluating model performance.

Additionally, the engineer will collaborate with cross-functional teams to ensure seamless integration of AI/ML components into the company's software offerings. Major Areas of Responsibility Design, Implement, and optimize workflows that incorporate large language models to automate and enhance product features. Leverage existing foundational models and adapt them to fit into various product requirements, ensuring alignment with business goals.

Collaborate with product managers, data scientist, and software engineers to integrate LLM-based automation into scalable solutions. Create and architect interpreters, Agented Systems, and integrate multi-hop RAG and other LLM experiences into existing systems to coordinate knowledge responses. Research and evaluate new technologies and methodologies in the LLM space to continuously improve product automation.

Work on the customization and fine-tunning of models to optimize performance for specific use cases. Develop, test, and deploy LLM-based services in production environments. Provide AI/ML technical leadership and mentorship to other engineers on the team.

Ensure that LLM integrations are efficient, scalable, and secure, adhering to industry best practices.

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