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Llm Prompt Engineer Jobs in Decatur, GA (NOW HIRING)

Deloitte

AI Data Engineer - Senior Consultant

Atlanta, GA · On-site

$100K - $138K/yr

AI Engineer Senior Consultant Our Deloitte Human Capital team transforms technology platforms ... prompt/context patterns. * Implement LLM application patterns including RAG, document ingestion ...

Deloitte

AI Data Engineer - Senior Consultant

Atlanta, GA · Hybrid

$100K - $138K/yr

AI Engineer Senior Consultant Our Deloitte Human Capital team transforms technology platforms ... prompt/context patterns. * Implement LLM application patterns including RAG, document ingestion ...

JConnect Infotech

Senior AI ML Engineer

Atlanta, GA

$100K - $138K/yr

Hands-on experience developing applications using LLMs, including prompt engineering and ... Architect and deliver end-to-end LLM-powered applications and agentic workflows using Python

Optimal Inc.

Agentic AI Engineer

Embry Hills, GA

... ML, LLM development, or agent-based systems * Strong hands-on experience with large language models (prompt engineering, fine-tuning, evaluation) * Experience building or working with agent ...

Deloitte

AI Engineer III

Atlanta, GA · Hybrid

AI Engineer III Position Summary Our Deloitte Human Capital team transforms technology platforms ... prompt/context patterns. * Implement LLM application patterns including RAG, document ingestion ...

Worth AI

Senior Agentic (AI) Engineer

Atlanta, GA · On-site +1

$100K - $138K/yr

Mentor engineers on agent patterns, prompt hygiene, eval discipline, and LLM failure modes. * Technology Stack * Languages: Python, Node.js, TypeScript * Agent / LLM frameworks: LangGraph, LangChain ...

Deloitte

AI Engineer Consultant

Atlanta, GA · Hybrid

We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ... prompt/context patterns. * Implement LLM application patterns including RAG, document ingestion ...

Worth AI

Senior Agentic (AI) Engineer

Atlanta, GA · Remote

$107K - $146K/yr

Mentor engineers on agent patterns, prompt hygiene, eval discipline, and LLM failure modes. * Technology Stack * Languages: Python, Node.js, TypeScript * Agent / LLM frameworks: LangGraph, LangChain ...

Intellectt INC

Agentic AI Engineer (Python)

Atlanta, GA · On-site

Proven experience developing LLM-driven or multi-agent AI systems. * Hands-on experience with ... Experience with prompt engineering, retrieval-augmented generation (RAG), and vector databases (e.g ...

OneTrust

Senior Software Engineer - SRE

Atlanta, GA

$54.75 - $72.75/hr

Well-developed insight of prompt engineering and evaluation of LLM outputs in the reliability workflow * Kubernetes and container orchestration (EKS/AKS/GKE) * Experience with distributed systems at ...

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All JobsLlm Prompt Engineer JobsLlm Prompt Engineer Jobs in Decatur, GA

Llm Prompt Engineer information

See Decatur, GA salary details

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$83

How much do llm prompt engineer jobs pay per hour?

As of Jun 20, 2026, the average hourly pay for llm prompt engineer in Decatur, GA is $56.84, according to ZipRecruiter salary data. Most workers in this role earn between $44.38 and $69.47 per hour, depending on experience, location, and employer.

What engineers make $500,000?

Senior engineers in specialized fields such as software engineering, data engineering, or machine learning engineering can earn $500,000 or more annually, especially with extensive experience, advanced skills, and in high-demand industries. Roles involving leadership, technical expertise, or working at major tech companies often have compensation packages reaching or exceeding this level.

What are some common challenges faced by LLM Prompt Engineers when designing effective prompts for large language models?

LLM Prompt Engineers often encounter challenges such as ensuring prompts are both clear and unambiguous to elicit accurate model responses, as well as avoiding bias or unintended outputs. Balancing creativity and specificity in prompt design can be tricky, especially when tailoring prompts for diverse user intents or specialized domains. Additionally, prompt engineers must frequently iterate and test their prompts, collaborating closely with data scientists and product teams to continually refine them based on observed model behavior and user feedback.

Which LLM is good for prompt engineering?

For a prompt engineer, large language models like OpenAI's GPT-4, Anthropic's Claude, and Google's PaLM are popular choices due to their advanced capabilities and flexibility. Selecting an LLM depends on factors such as API access, customization options, and the specific application requirements. Familiarity with prompt design and model tuning is essential for effective prompt engineering.

What is an LLM Prompt Engineer?

An LLM Prompt Engineer is a professional who specializes in designing, testing, and optimizing prompts for large language models (LLMs) such as GPT-4. Their role involves crafting effective instructions and queries to guide the model's output for specific applications, ensuring accuracy, relevance, and reliability. They may also analyze model behavior, implement prompt-based workflows, and collaborate with developers to integrate LLMs into products or services. The goal is to maximize the performance and efficiency of language models in various real-world contexts.

How much do LLM engineers make?

LLM prompt engineers typically earn between $80,000 and $150,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in AI and machine learning can command higher salaries, often exceeding $180,000. Compensation may also include bonuses and stock options in tech-focused organizations.

Are prompt engineers still in demand?

Prompt engineers are currently in demand as organizations seek to optimize AI language models for various applications. The role requires skills in natural language processing, prompt design, and familiarity with large language models like GPT, making it a valuable position in AI development teams.

What are the key skills and qualifications needed to thrive as an LLM Prompt Engineer, and why are they important?

To thrive as an LLM Prompt Engineer, you need a deep understanding of natural language processing, prompt engineering strategies, and proficiency in programming languages such as Python, often supported by a degree in computer science or a related field. Familiarity with machine learning frameworks (like TensorFlow or PyTorch), large language model APIs, and version control systems is typically required. Strong analytical thinking, creativity, and effective communication are crucial soft skills for crafting precise prompts and collaborating with cross-functional teams. These skills ensure the development of effective, ethical, and high-performing AI-powered solutions that meet diverse user needs.

What is the difference between Llm Prompt Engineer vs Data Scientist?

AspectLlm Prompt EngineerData Scientist
Required CredentialsBachelor's in CS, AI, or related fields; familiarity with NLP and AI toolsBachelor's or higher in CS, Statistics, or related fields; strong programming and statistical skills
Work EnvironmentAI labs, tech companies, startups focusing on NLP and AI modelsData analysis, modeling, and visualization in various industries like finance, healthcare, tech
Employer & Industry UsagePrimarily in AI development, NLP projects, and machine learning teamsAcross industries for data analysis, predictive modeling, and decision support

While both roles involve working with data and AI, Llm Prompt Engineers focus on designing prompts for language models, whereas Data Scientists analyze data to derive insights. The roles share similar educational backgrounds and work environments but differ in their core tasks and industry applications.

What job categories do people searching Llm Prompt Engineer jobs in Decatur, GA look for? The top searched job categories for Llm Prompt Engineer jobs in Decatur, GA are:
What cities near Decatur, GA are hiring for Llm Prompt Engineer jobs? Cities near Decatur, GA with the most Llm Prompt Engineer job openings:
Llm Prompt Engineer jobs near you
Machine Learning Engineer - LLMs and Agentic

Machine Learning Engineer - LLMs and Agentic

Oversight Systems Inc

Atlanta, GA • On-site

Full-time

Posted 3 days ago

Job description

About Oversight

Oversight is the world’s leading provider of AI-based spend management and risk mitigation solutions for large enterprises. Based in Atlanta, GA, Oversight works with many of the world’s most innovative companies and government agencies to digitally transform their spend audit and financial control processes.

Oversight’s AI-powered platform works across our customers’ financial systems to continuously monitor and analyze all spend transactions for fraud, waste, and misuse. With a consolidated, consistent view of risk across their enterprise, customers can prevent financial loss and optimize spend while strengthening the controls that improve compliance. Learn More.

Position Overview:

We are seeking a skilled and forward-looking ML Engineer with experience in Large Language Models (LLMs), generative AI, and agentic architectures to join our growing R&D and Applied AI team. This role is critical in helping Oversight deliver the next generation of agentic AI systems for enterprise spend management and risk controls.

The ideal candidate has a strong foundation in machine learning, modern deep learning frameworks, and data pipelines, coupled with hands-on experience experimenting with LLMs, small language models (SLMs), multi-agent frameworks, and retrieval-augmented generation (RAG).

You will work closely with AI/ML researchers, data engineers, and product teams to design, implement, and optimize models that power autonomous exception resolution, anomaly detection, and explainable insights. This is a hands-on engineering role where you will not only build and scale ML systems but also actively contribute to cutting-edge applied research in agentic AI.

Core ML/LLM Engineering
  • 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, MCP to prototype and optimize multi-agent workflows.
  • Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions.
  • Integrate memory, evidence packs, and explainability modules into agentic pipelines.
  • Work hands-on with multiple LLM ecosystems:
    • OpenAI GPT models (GPT-4, GPT-4o, fine-tuned GPTs).
    • Anthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows).
    • Google Gemini (multimodal reasoning, advanced RAG integration).
    • Meta LLaMA (fine-tuned/custom models for domain-specific tasks).
Data & Infrastructure
  • Collaborate with Data Engineering to build and maintain real-time and batch data pipelines that serve ML/LLM workloads.
  • Conduct feature engineering, preprocessing, and embeddings generation for structured and unstructured data.
  • Implement model monitoring, drift detection, and retraining pipelines.
  • Leverage cloud ML platforms (AWS Sagemaker, Databricks ML) for experimentation and scaling.
Research & Applied Innovation
  • Explore and evaluate emerging LLM/SLM architectures and agent orchestration patterns.
  • Experiment with generative AI and multimodal models to extend capabilities beyond text (images, structured financial data).
  • Collaborate with R&D to prototype autonomous resolution agents, anomaly detection models, and reasoning engines.
  • Translate research prototypes into production-ready components.
Collaboration & Delivery
  • Work cross-functionally with R&D, Data Science, Product, and Engineering to deliver business-aligned AI features.
  • Participate in design reviews, architecture discussions, and model evaluations.
  • Document processes, experiments, and results effectively for knowledge sharing.
  • Mentor junior engineers and contribute to ML engineering best practices.


Education, Experience and Skills

Required

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.
  • 3+ years of experience building and deploying ML systems.
  • Proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.
  • Hands-on experience with LLMs/SLMs (fine-tuning, prompt design, inference optimization).
  • Demonstrated experience with at least two of the following ecosystems:
    1. OpenAI GPT models (chat, assistants, fine-tuning).
    2. Anthropic Claude (safety-first AI for reasoning and summarization).
    3. Google Gemini (multimodal reasoning, enterprise-scale APIs).
    4. Meta LLaMA (open-source, fine-tuned models).
  • Familiarity with vector databases, embeddings, and RAG pipelines.
  • Ability to work with structured and unstructured data at scale.
  • Knowledge of SQL and distributed data frameworks (Spark, Ray).
  • Strong understanding of ML lifecycle: data prep, training, evaluation, deployment, monitoring.
Preferred Qualifications
  • Experience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).
  • Knowledge of AI safety, guardrails, and explainability techniques.
  • Hands-on experience deploying ML/LLM solutions in cloud environments (AWS, GCP, Azure).
  • Experience with CI/CD for ML (MLOps), monitoring, and observability.
  • Familiarity with anomaly detection, fraud/risk modeling, or behavioral analytics.
  • Contributions to open-source AI/ML projects or publications in applied ML research.