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Entry Level Retrieval Augmented Generation Jobs in Atlanta, GA

Spruce Infotech

Agentic Gen AI Engineer

Alpharetta, GA · On-site

... Retrieval-Augmented Generation (RAG) and reasoning pipelines to ensure grounded, reliable, and adaptive agent behavior. • Collaborate closely with GenAI engineers, application teams, MLOps, product ...

ITBMS Inc.

AI Engineer

Alpharetta, GA · On-site

Technical Skills AI & Agents LLMs, Agentic AI, Multi-Agent Systems, Prompt Engineering, Retrieval-Augmented Generation (RAG), Semantic Kernel, Lang-Graph, Microsoft Auto-Gen, AI Governance ...

Aptean

AI Implementation Engineer

Alpharetta, GA · On-site

$120 - $180/hr

Develop retrieval-augmented generation pipelines, integrate enterprise data sources, and manage vector databases. * Deploy solutions, monitor performance, troubleshoot issues, and update them to ...

Aptean

AI Implementation Engineer

Alpharetta, GA · On-site

Develop retrieval-augmented generation pipelines, integrate enterprise data sources, and manage vector databases. * Deploy solutions, monitor performance, troubleshoot issues, and update them to ...

UNAVAILABLE

AI Implementation Engineer

Alpharetta, GA · On-site

$120 - $160/hr

Develop retrieval-augmented generation pipelines, integrate enterprise data sources, and manage vector databases. * Deploy solutions, monitor performance, troubleshoot issues, and update them to ...

Posted today

Aptean

AI Implementation Engineer

Alpharetta, GA

Develop retrieval-augmented generation pipelines, integrate enterprise data sources, and manage vector databases. * Deploy solutions, monitor performance, troubleshoot issues, and update them to ...

Aptean

AI Implementation Engineer

Alpharetta, GA · On-site

$120 - $170/hr

Develop retrieval-augmented generation pipelines, integrate enterprise data sources, and manage vector databases. * Deploy solutions, monitor performance, troubleshoot issues, and update them to ...

Aptean

AI Implementation Engineer

Alpharetta, GA

Develop retrieval-augmented generation pipelines, integrate enterprise data sources, and manage vector databases. * Deploy solutions, monitor performance, troubleshoot issues, and update them to ...

Collabera

AI/ML Engineer

Atlanta, GA · On-site

$50 - $70/hr

Experience building RAG (Retrieval-Augmented Generation) pipelines for AI applications. Experience developing AI agents or agentic workflows using frameworks such as Lang Chain, LangGraph, CrewAI ...

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Entry Level Retrieval Augmented Generation information

What is an entry level retrieval augmented generation job?

Entry level retrieval augmented generation jobs involve assisting in the development and optimization of AI systems that combine information retrieval techniques with generative models. Employees in these roles typically help build, test, and maintain systems where AI retrieves relevant data from large databases to enhance the accuracy and relevance of generated responses. These positions often require basic skills in programming, machine learning, and familiarity with natural language processing. They are ideal for recent graduates or those new to AI, offering opportunities to learn about modern AI architectures and contribute to innovative projects. Entry level workers may work under the guidance of senior engineers or researchers, supporting experimentation and evaluation tasks.

What are the key skills and qualifications needed to thrive as an entry level retrieval augmented generation specialist?

To thrive as an Entry Level Retrieval Augmented Generation Specialist, you need a foundational understanding of natural language processing (NLP), information retrieval, and basic programming skills, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, vector databases (like FAISS or Pinecone), and frameworks for large language models (LLMs) is typically required. Strong problem-solving abilities, attention to detail, and effective communication help you collaborate and troubleshoot solutions in team environments. These skills and qualities are crucial for building reliable RAG systems that deliver accurate and relevant information to users.

What are some common challenges faced by entry-level professionals working in retrieval augmented generation roles?

Entry-level professionals in Retrieval Augmented Generation (RAG) often encounter challenges such as understanding how to effectively combine information retrieval systems with large language models and adapting to rapidly evolving technologies. Balancing accuracy and efficiency when designing or fine-tuning retrieval pipelines can also be a learning curve. Additionally, you may need to collaborate closely with data engineers, machine learning specialists, and product teams to ensure the RAG system aligns with business requirements. Staying proactive in learning and engaging with peers can help overcome these challenges and accelerate career growth.

What is the difference between Entry Level Retrieval Augmented Generation vs Entry Level Data Scientist?

AspectEntry Level Retrieval Augmented GenerationEntry Level Data Scientist
Required CredentialsBasic programming, understanding of NLP and AI conceptsBachelor's in Data Science, Computer Science, or related field
Work EnvironmentTech companies, AI startups, research labsTech firms, finance, healthcare, consulting
Industry UsageAI development, NLP applications, chatbot creationData analysis, predictive modeling, data-driven decision making

Entry Level Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with generative AI, requiring knowledge of NLP and programming. Entry Level Data Scientist involves analyzing data, building models, and deriving insights, often with a broader data analysis skill set. While both roles require technical skills, Retrieval Augmented Generation is more specialized in AI model development, whereas Data Scientists work across various data projects.

What are the most commonly searched types of Retrieval Augmented Generation jobs in Atlanta, GA?

The most popular types of Retrieval Augmented Generation jobs in Atlanta, GA are:

What are popular job titles related to Entry Level Retrieval Augmented Generation jobs in Atlanta, GA?

For Entry Level Retrieval Augmented Generation jobs in Atlanta, GA, the most frequently searched job titles are:

What job categories do people searching Entry Level Retrieval Augmented Generation jobs in Atlanta, GA look for?

The top searched job categories for Entry Level Retrieval Augmented Generation jobs in Atlanta, GA are:

Entry Level Retrieval Augmented Generation jobs near you
Infographic showing various Entry Level Retrieval Augmented Generation job openings in Atlanta, GA as of August 2026, with employment types broken down into 67% Full Time, 30% Part Time, and 3% Contract. Highlights an 69% Physical, 3% Hybrid, and 28% Remote job distribution.

Junior AI Engineer, Financial Services

Jobtailor

Atlanta, GA • On-site

$150 - $190/hr

Other

Posted 3 days ago

New

Job description

  • Add AI capabilities to existing full-stack applications, including LLM-powered features, workflows, and interfaces
  • Build and improve retrieval-augmented generation (RAG) systems with a Senior AI Engineer
  • Develop chunking and embedding strategies, retrieval-quality processes, and evaluations
  • Manage context windows and model inputs across text, documents, and images
  • Help design agentic workflows involving multi-step LLM pipelines, tool use, and orchestration
  • Prompt-engineer and evaluate LLM workflows
  • Write clean, testable services and data pipelines
  • Translate investment and operations workflow needs into concrete technical problems
  • Take sprint tasks directly within 90 days and contribute to active RAG or agentic workflow projects
  • Assume increasing ownership of a RAG or agentic workflow component and contribute to evaluation and testing pipelines within one year
  • Work closely with a Senior AI Engineer on a small embedded technology team
Requirements
  • Strong software engineering fundamentals in Node.js and TypeScript
  • Experience with REST APIs, async and promise-based concurrency, and testing frameworks such as Jest or Vitest
  • Everyday comfort with Git
  • Exposure to cloud infrastructure, ideally AWS
  • Exposure to AWS AI and LLM services such as Bedrock, Bedrock AgentCore, and Knowledge Bases for Bedrock
  • Practical experience with LLM APIs such as OpenAI, Anthropic, or similar
  • Experience with function calling, tool use, structured outputs, streaming responses, token usage, and context-window limits
  • Hands-on experience building a real LLM project, prototype, or shipped feature
  • Experience designing LLM-in-the-loop evaluation and testing pipelines
  • Experience with golden datasets, LLM-as-judge scoring, and regression tests for prompts and retrieval
  • Familiarity with tools such as promptfoo, Braintrust, or Ragas
  • Comfort using AI-native development tools such as Claude Code or Cursor
  • Ability to measure prompt, retrieval, and model changes using evaluation scores, latency, or cost-per-call metrics
  • Coachability and initiative
  • Clear communication with non-technical investment and operations professionals
  • Bonus: familiarity with Python, AWS Amplify, RAG mechanics, vector stores, hybrid search, reranking, agent/RAG frameworks, production AI features, data sensitivity, or MCP
Core Competencies

Demonstrates strong software engineering fundamentals with expertise in Node.js and TypeScript, alongside practical experience in developing and evaluating LLM workflows and retrieval-augmented generation systems. Capable of translating complex operational needs into technical solutions while effectively collaborating with cross-functional teams.

Highest-signal resume keywords
  • Node.js
  • TypeScript
  • LLM APIs
  • AWS AI Services
  • RAG Systems
ATS Optimization Keywords Hard Skills
  • Software Engineering Fundamentals
  • REST APIs
  • Async Programming
  • Testing Frameworks
  • Function Calling
  • Tool Use
  • Structured Outputs
  • Streaming Responses
  • Context-Window Management
  • Evaluation Metrics
Soft Skills
  • Coachability
  • Initiative
  • Clear Communication
Industry Keywords
  • Retrieval-Augmented Generation
  • LLM Workflows
  • Agentic Workflows
  • Golden Datasets
  • LLM-as-Judge Scoring
Tools & Technologies
  • Git
  • AWS
  • Promptfoo
  • Braintrust
  • Ragas
  • Claude Code
  • Cursor
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