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Rag Llm Jobs (NOW HIRING)

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:

dhanutek

AI / LLM Engineer Location: Seattle/NJ/Dallas, TX Duration: 12 plus months with possible extension ... RAG systems, document AI, voice AI

Kai Cyber, Inc.

Senior Data Engineer, AI Platform

San Jose, CA · On-site

$124K - $168K/yr

Build and optimize retrieval pipelines for RAG and LLM-based applications * Design and manage vector data pipelines (embedding generation, indexing, storage, retrieval) * Implement hybrid retrieval ...

Siemens Healthineers

AI LLM Engineer

Atlanta, GA · On-site +1

Strong understanding of LLM architectures, RAG, embeddings, vector databases, and prompt ... engineering * Experience designing scalable, production grade systems in enterprise environments

HeadHR

NY · On-site

$64K - $86K/yr

Hands‑on experience building LLM applications (RAG, agents, LLMOps) * Strong Python + production systems (APIs, CI/CD, monitoring, debugging) * Experience with data pipelines, SQL, and data ...

Prophecy Technologies

AI/ML Engineer

Plano, TX · On-site

$109K - $131K/yr

This role offers the opportunity to work on cutting-edge AI initiatives, including Generative AI, Retrieval-Augmented Generation (RAG), LLM fine-tuning, and intelligent enterprise applications. Key ...

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Rag Llm information

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How much do rag llm jobs pay per year?

As of Sep 11, 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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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.

Agentic AI Engineer Co-Op

Remote

Campbell Soup Company
See more jobs at Campbell Soup Companyexternal

Full-time

Re-posted 28 days ago

Job description

Since 1869, we've connected people through food they love. We're proud to be stewards of amazing brands that people trust. Our portfolio includes the iconic Campbell's brand, as well as Cape Cod, Chunky, Goldfish, Kettle Brand, Lance, Late July, Pacific Foods, Pepperidge Farm, Prego, Pace, Rao's Homemade, Snack Factory, Snyder's of Hanover. Swanson, and V8.
Here, you will make a difference every day. You will be supported to build a rewarding career with opportunities to grow, innovate and inspire. Make history with us.
An Agentic AI Engineer designs, builds, and deploys autonomous AI systems that can reason, plan, use tools, and execute multi-step workflows with minimal human intervention . Unlike traditional AI/ML engineers who focus on model training and prediction, agentic AI engineers orchestrate goal-driven workflows that integrate models, tools, memory, and business logic to achieve objectives dynamically
Core Responsibilities
• Design & Develop Agentic Systems: Build intelligent agents capable of autonomous planning, reasoning, and task execution, often using LLMs (e.g., GPT-class, LLaMA), multi-modal models, and autonomous workflows
• Orchestration & Frameworks: Implement agent orchestration using frameworks like LangChain, AutoGen, CrewAI, Semantic Kernel, or custom solutions
• Retrieval-Augmented Generation (RAG): Design and optimize RAG pipelines for enhanced reasoning with external knowledge, including document ingestion, chunking, embeddings, vector stores, and retrieval ranking
• Tool & Memory Integration: Develop agents that call APIs, databases, and other tools, maintain memory, and adapt based on outcomes
• Evaluation & Monitoring: Create evaluation frameworks for accuracy, grounding, latency, and cost; build observability for agent behavior and failure modes
• Model Adaptation: Fine-tune or adapt foundation models (e.g., via LoRA, adapters) for domain-specific use cases
• Production Deployment: Deploy GenAI/agentic systems in cloud-native environments with CI/CD, versioning, and runtime safeguards
• Cross-Functional Collaboration: Work with data scientists, ML engineers, product teams, and governance/compliance stakeholders
Required Skills & Experience
• 2+ years in AI/ML system design, deployment, or autonomous agent development
• Programming: Proficiency in Python (and sometimes Java, C#) for AI/ML solution development
• Agent & Workflow Expertise: Experience with agent orchestration frameworks and multi-agent communication protocols
• RAG & LLM Integration: 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: Understanding of responsible AI, security, and compliance in regulated domains (e.g., retail)
The Company is committed to providing equal opportunity for employees and qualified applicants in all aspects of the employment relationship, including consideration for employment, without regard to race, color, sex, sexual orientation, gender identity, national origin, citizenship, marital status, protected veteran status, disability, age, religion, or any other classification protected by law.

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