1

Llm Ml Rag Jobs in Georgia (NOW HIRING)

AI/ML Engineer

Atlanta, GA ยท On-site +1

As an AI/ML Engineer, you'll join a collaborative team of technologists, data scientists, and ... Build and enhance Generative AI, LLM, and Retrieval-Augmented Generation (RAG) applications ...

Software Engineer, AI/ML

Atlanta, GA ยท On-site

$102K - $160K/yr

Develop and integrate AI/ML and LLM-based solutions-including RAG/GraphRAG architectures, vector embeddings, and semantic retrieval-to improve translation and localization quality. * Build and ...

Software Engineer, AI/ML

Atlanta, GA ยท On-site +1

$102K - $160K/yr

Develop and integrate AI/ML and LLM-based solutions-including RAG/GraphRAG architectures, vector embeddings, and semantic retrieval-to improve translation and localization quality. * Build and ...

Senior AI/ML Engineer

Atlanta, GA ยท On-site

$100K - $138K/yr

You will also contribute to LLM-based solutions that enable natural language querying of CDP data ... Build and support RAG pipelines to enrich customer profiles with contextual data from unstructured ...

Contribute to the design, training, fine-tuning, and deployment of ML/LLM models for production. * Implement RAG pipelines using vector databases. * Work with frameworks like LangChain, LangGraph ...

Senior Machine Learning Engineer (Nova)

Atlanta, GA ยท On-site

$100K - $138K/yr

Experience building ML or LLM platforms, tooling, or developer-facing frameworks. * Prior work with embeddings, search-ranking systems, or advanced RAG architectures. * Familiarity with event-driven ...

The Data Scientist (AI/ML) leverages advanced analytics, statistical modeling, machine learning ... LLM) solutions, retrieval-augmented generation (RAG), knowledge graph technologies, and emerging ...

... ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM ...

... ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM ...

next page

Showing results 1-20

Llm Ml Rag information

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 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 is an llm ml rag job?

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 cities in Georgia are hiring for Llm Ml Rag jobs? Cities in Georgia with the most Llm Ml Rag job openings:

Data Scientist / AI Engineer (LLM & Agentic AI)

Tror AI for everyone

Atlanta, GA โ€ข On-site

Contractor

Re-posted 19 days ago


Job description

Role: Data Scientist / AI Engineer (LLM & Agentic AI)

Location: Atalanta, GA

Duration: Contract

Only H1B

Summary:
Looking for candidates with strong Python-based data science and machine learning experience, along with hands-on expertise in LLMs and agentic AI development on platforms like Databricks or Cloudera.

Key Responsibilities

  • Build and deploy ML/AI models using Python
  • Develop LLM-powered applications (RAG, chatbots, copilots)
  • Design agentic AI workflows using tools like LangChain or LlamaIndex
  • Work with large-scale data pipelines and distributed systems (Spark/PySpark)
  • Integrate LLM APIs (e.g., OpenAI)

Required Skills

  • Strong Python + ML/NLP fundamentals
  • Experience with Generative AI / LLMs
  • Hands-on with Databricks or Cloudera
  • Knowledge of RAG, prompt engineering, vector DBs