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Rag Llm Jobs (NOW HIRING)

JS Consulting

Data Scientist

Washington, DC · On-site

Experience with LoRA, LangChain, RAG, LLM Fine Tuning and PEFT, Knowledge Graphs. * Strong skills in developing GraphRAG, Chain of Thought (CoT), Tree of Thought (ToT), Reinforcement learning and AI ...

Precision Technologies Corp

AI Engineer

Dallas, TX · On-site

Contract Key Skills - AI, Python, Rag, LLM Overview We are seeking an AI Engineer with proven experience in building and scaling AI-powered applications . This role combines hands-on development with ...

3M

Enterprise Search Tech Lead

Maplewood, MN · On-site

Driving platform evolution toward hybrid retrieval (lexical + semantic/vector search) to support RAG, LLM grounding, and agentic AI use cases. * Creating and maintaining comprehensive technical ...

Campbell's

Agentic AI Engineer Co-Op

Hands-on with RAG architectures, evaluation methodologies, and LLM integration Cloud & DevOps: Experience with cloud platforms (e.g., Azure, AWS) and CI/CD pipelines Governance & Compliance:

Campbell's

Agentic AI Engineer Co-Op

Hands-on with RAG architectures, evaluation methodologies, and LLM integration Cloud & DevOps: Experience with cloud platforms (e.g., Azure, AWS) and CI/CD pipelines Governance & Compliance:

3M

Enterprise Search Tech Lead

Maplewood, MN · On-site +1

Driving platform evolution toward hybrid retrieval (lexical + semantic/vector search) to support RAG, LLM grounding, and agentic AI use cases. * Creating and maintaining comprehensive technical ...

3M

Enterprise Search Tech Lead

Austin, TX · On-site +1

Driving platform evolution toward hybrid retrieval (lexical + semantic/vector search) to support RAG, LLM grounding, and agentic AI use cases. * Creating and maintaining comprehensive technical ...

Dutech Systems

Stay current with emerging Generative AI, Agentic AI, RAG, LLM, and automation technologies. Required Qualifications * 8+ years of professional technology or software development experience. * Strong ...

Campbell Soup Company

Agentic AI Engineer Co-Op

Hands-on with RAG architectures, evaluation methodologies, and LLM integration • Cloud & DevOps: Experience with cloud platforms (e.g., Azure, AWS) and CI/CD pipelines • Governance & Compliance:

Ignite Digital

Mid Data Scientist

Washington, DC · Hybrid

LoRA, LangChain, RAG, LLM Fine Tuning). * Hands-on experience with data visualization tools (e.g. Qlik, Power BI, Tableau, Databricks). * Strong quantitative and analytic abilities. Desired Skills:

High Bridge Consulting LLC

Sr AI Data Engineer

Brooklyn, NY · On-site +1

$50 - $80/hr

Hands-on experience with RAG, LLM integration, and enterprise AI applications. * Experience with Databricks, Azure, AWS, or GCP. * Strong Python and data engineering skills. * Experience implementing ...

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Rag Llm information

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$45K

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$110K

How much do rag llm jobs pay per year?

As of Sep 12, 2026, the average yearly pay for rag llm 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 is a RAG LLM?

RAG LLMs, or Retrieval-Augmented Generation Large Language Models, are advanced AI systems that combine the strengths of traditional language models with external data retrieval systems. They work by first searching a relevant database or knowledge base for up-to-date information, and then using a language model to generate responses based on both the retrieved content and their own training. This approach helps LLMs provide more accurate, current, and contextually relevant answers, especially for specialized or rapidly changing topics. RAG LLMs are widely used in customer support, research, and enterprise applications to improve information accuracy and reliability.

How do RAG LLM engineers collaborate with data scientists and product teams to improve retrieval-augmented generation systems?

RAG LLM engineers often work closely with data scientists to fine-tune retrieval mechanisms, optimize model performance, and evaluate system outputs. They also collaborate with product teams to understand user needs, integrate feedback, and ensure the system delivers relevant, accurate information. Regular cross-functional meetings and code reviews are common, fostering a collaborative environment focused on continuous improvement and innovation in response to real-world challenges.

What are the key skills and qualifications needed to thrive as a Retrieval-Augmented Generation (RAG) LLM engineer?

To thrive as a Retrieval-Augmented Generation (RAG) LLM Engineer, you need a strong background in natural language processing, machine learning, and software development, often supported by a degree in computer science or a related field. Familiarity with frameworks like PyTorch, Hugging Face Transformers, vector databases, and cloud platforms, along with experience deploying large language models, is essential. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaboration and innovation in this fast-evolving space. These skills ensure the development of robust, scalable, and accurate retrieval-augmented AI systems that meet real-world information needs.

What is the difference between Rag Llm vs Data Scientist?

AspectRag LlmData Scientist
Required CredentialsTypically a master's or PhD in AI, machine learning, or related fieldsUsually a master's or PhD in data science, statistics, or computer science
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, tech firms, consulting
Industry UsageAI research, natural language processing, machine learning projectsData analysis, predictive modeling, data-driven decision making

Rag Llm and Data Scientist roles often overlap in AI and data analysis fields, but Rag Llm focuses more on language models and AI research, while Data Scientists handle broader data analysis and modeling tasks. Both require advanced degrees and work in tech-driven environments, but their core responsibilities differ in scope and application.

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Rag Llm jobs near you
Infographic showing various Rag Llm job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 95% Full Time, 1% Part Time, and 3% Contract. Highlights an 74% Physical, 5% Hybrid, and 21% Remote job distribution, with an average salary of $75,300 per year, or $36.2 per hour.

Data Scientist

Washington, DC • On-site

JS Consulting
Custom Software Development Services • 1 - 10 employees
Learn more about JS Consultingexternal

Contractor

Re-posted 2 days ago

Job description

Job Title- Data Scientist

Project Location – Onsite in Washington, District of Columbia

Duration- 6+ months contract

Visa- USC

Must have PHD

 Minimum Qualifications:

  • Work or educational background in one or more of the following areas: machine learning, computational linguistics, deep learning, ratification intelligence, data science and/or data analytic, generative AI, symbolic AI, causal AI, operations research, computer science, Mathematics, business analytics, or knowledge management.
  • Demonstrated experience programming with R/Python, Linux, and Spark in AWS cloud environment, or knowledge and algorithmic design experience in Python (3+ years)
  • Proficient with Amazon AWS Sagemaker, Jupyter Notebook and Python Scikit, Deep Learning, Machine Learning tools such as TensorFlow
  • Experience with image processing models such as Coco, CLIP, ResNet or comparable models
  • Demonstrated experience with machine learning techniques including natural language processing, and Large language Models (GPTv4-o1, o3, OpenAI APIs, Llama, Claude, etc).
  • Experience developing AI agents and development proficiency using agentic programming
  • Proficient in Natural language processing (NLP) and Natural language generation (NLG) including prior projects in any of the following categories: top modeling of text, sentiment analysis of text, part of speech tagging, Name Entity Recognition (NER), Bag of Words, text extraction
  • Experience building and working with any of these components: Vector DB, BERT, RoBERTa (or comparable tools), Spacy, LLM and GenAI tools. Experience with LoRA, LangChain, RAG, LLM Fine Tuning and PEFT, Knowledge Graphs.
  • Strong skills in developing GraphRAG, Chain of Thought (CoT), Tree of Thought (ToT), Reinforcement learning and AI development architectures with Human-in-the-Loop (HITL
  • Demonstrated experience with SQL and any relational database technologies, such as Oracle, PostgreSQL, MySQL, RDS, Redshift, Hadoop EMR, Hive, etc.
  • Demonstrated experience processing structured and unstructured data sources, data cleansing, data normalization and prep for analysis
  • Demonstrated experience with code repositories and build/deployment pipelines, specifically Jenkins and/or Git/GitHub/GitLab.
  • Demonstrated experience using Tableau, or Kibana, Quicksights or other similar data visualizations tools.
  • Very comfortable working with ambiguity (e.g. imperfect data, loosely defined concepts, ideas, or goals)

 Qualifications & Requirements

  • Education: MS in Computer Science, Statistics, Math, Engineering, or related field, PhD required.
  • 3+ years of relevant experience in building large scale machine learning or deep learning models and/or systems
  • 1+ year of experience specifically with deep learning (e.g., CNN, RNN, LSTM)
  • 1+ year of experience building NLP and NLG tools.
  • Experience with wide range of LLMs (Llama, Claude, OpenAI, Cohere, etc.), LoRA, LangChain, RAG, LLM Fine Tuning and PEFT are preferred.
  • Demonstrated skills with Jupyter Notebook, AWS Sagemaker, or Domino Datalab or comparable environments
  • Passion for solving complex data problems and generating cross-functional solutions in a fast-paced environment
  • Knowledge in Python and SQL, object oriented programming, service oriented architectures
  • Strong scripting skills with Shell script and SQL
  • Strong coding skills and experience with Python (including SciPy, NumPy, and/or PySpark) and/or Scala.
  • Knowledge and implementation experience with NLP techniques (topic modeling, bag of words, text classification, TF/IDF, Sentiment analysis) and NLP technologies such as Python NLTK, or Spacy or comparable technologies
  • Knowledge and implementation experience with statistical and machine learning models (regression, classification, clustering, graph models, etc.)

 Preferred Qualifications

  • Hands on experience building models with deep learning frameworks like Tensorflow, Keras, Caffe, PyTorch, Theano, H2O, or similar
  • Experience with LLM Agents, Agentic programming
  • Experience with search architecture (for instance: Solr, ElasticSearch, AWS OpenSearch)
  • Experience with building querying ontologies such as Zeno, OWL, RDF, SparQL or comparable are preferred
  • Knowledge & experience with microservices, service mesh, API development and test automation are preferred
  • Demonstrated experience using Docker, Kubernetes, and/or other similar container frameworks are preferred

 Additional Job Qualifications:

  • Ability to translate business ideas into analytics models that have major business impact.
  • Demonstrated experience working with multiple stakeholders.
  • Demonstrated communication skills, e.g. explaining complex technical issues to more junior data scientists, in graphical, verbal, or written formats.
  • Demonstrated experience developing tested, reusable and reproducible work.