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Llm Ml Rag Jobs (NOW HIRING)

Futran Tech Solutions Pvt. Ltd.

AI/ML Engineer

MLOps / Platform • Implement CI/CD for ML/LLM workloads using MLflow, Vertex, SageMaker, Databricks, Airflow, Argo, and Kubernetes. • Apply LLM Ops foundations for RAG and GenAI use cases.

Symhas

Gen AI Lead - RAG Specialist

Pleasanton, CA · On-site

... AI/ML or software engineering experience * 3+ years building and deploying RAG systems in ... Proficiency in Python, LLM APIs, and document processing pipelines * US Green Card or Citizenship ...

New

Apex Informatics

AI/ ML engineer

Title: AI/ML Engineer Location: NJ/ TX Overall Technology Snapshot * AWS Cloud * Base Python ... FAST API * LLM Pipeline in AWS Environment * RAG Architecture, Optimization, and Tuning

Carter Support Services

AI/ML Engineer

Burbank, CA · On-site

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 ...

CCube Solutions

AI/ML Tech Lead

Raleigh, NC · On-site

$150K - $200K/yr

Position:- AI/ML Tech Lead Location: Raleigh, NC (Hybrid) Job Type: Full Time/W2 Only Exp Level- 8 ... LLM understanding, RAG. Architecture end to end, built systems. We are seeking a highly skilled AI ...

Futran Tech Solutions Pvt. Ltd.

AI/ML Engineering

AI / ML Engineering Execution • Oversee development of: o Machine learning models (supervised / unsupervised) o Generative AI and LLM based solutions o Retrieval Augmented Generation (RAG ...

Carter Support Services

AI/ML Engineer

Burbank, CA · On-site

Responsibilities : • Design and implement AI/ML solutions using Python and modern ML frameworks • Develop and optimize prompt engineering strategies for LLM-based systems • Build and deploy RAG ...

Ccube

AI & ML Tech Lead/Architect

Durham, NC · On-site

$150K - $225K/yr

Position:- AI & ML Tech Lead Location: Raleigh, NC (Hybrid) Job Type: Full Time/W2 Only Exp Level ... LLM understanding, RAG. Architecture end to end, built systems. We are seeking a highly skilled AI ...

New

Interon IT Solutions

AI/ML Engineer

Reston, VA · On-site

AI/ML Engineer (Python, AWS, GenAI) Location: Reston, VA (In-person interviews required) Candidate ... Architect and operationalize RAG pipelines , embeddings, vector databases, and LLM-powered ...

Bomnin Chevrolet Dadeland

Sr. AI/ML Engineer (LLM)

Miami, FL · On-site

$99K - $137K/yr

Create and architect interpreters, Agented Systems, and integrate multi-hop RAG and other LLM ... Provide AI/ML technical leadership and mentorship to other engineers on the team. * Ensure that LLM ...

Inspire11

ML Engineering Consultant

Chicago, IL · On-site

... LLM-based applications, RAG infrastructure, and common search paradigms • Ensuring security and compliance • Preprocessing and analyzing datasets • Developing and fine-tuning ML models to ...

Diverse Lynx

AI/ ML Engineer

Milford, OH · On-site

Diverse Lynx is seeking a Senior AI ML Engineer to architect and deliver LLM-powered applications. The role involves designing and implementing RAG pipelines, integrating AI systems, and establishing ...

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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.

Data Scientist w Generative AI (with AI , ML, and LLM - not ML Ops)

Kanak Elite Services Inc

Seattle, WA • On-site

Contractor

Posted 24 days ago

Job description

Hello There,

My name is Himanshu Sharma, and I serve as the Recruitment Lead at Kanak-IT INC. I am reaching out to share an excellent career opportunity for the role of Data Scientist with our esteemed client. If you are interested then please share your updated resume at Himanshu01@kanakits.com .

Job Description

Position           : Data Scientist with Generative AI (with AI , ML, and LLM – not ML Ops)

Location          : Seattle, WA Onsite (as well as final in-person interview – no exceptions) NEED LOCAL HERE

Duration         : Twelve months contract, will extend

Exp required   : 13+ Years

Interview Will be:

  1. One tech round (including 45–60-minute GenAI coding challenge) - teams
  2. One round with the account manager – team – 5-10 mins – culture fit
  3. One round with client – teams – culture fit
  4. Final meet and greet (not technically an interview) w the buyer – onsite, no exceptions

Description

    1. Build and deploy Generative AI solutions using Amazon Bedrock, SageMaker, and other AWS AI/ML services.
    2. Evaluate and integrate foundation models (Claude, Titan, Mistral, LLaMA, etc.) available in Bedrock for enterprise use cases (chatbots, summarization, RAG, etc.).
    3. Fine-tune and prompt engineer large language models (LLMs) for domain-specific needs.
    4. Work on GenAI pipelines: data preprocessing, prompt tuning, retrieval augmentation (RAG), and model evaluation.
    5. Optimize cost and performance of GenAI workloads in the AWS ecosystem.
    6. Collaborate with product and data teams to design AI-driven applications.