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Python Llm Jobs in Atlanta, GA (NOW HIRING)

State Farm

Lead GenAI Engineer (LLM)

Dunwoody, GA · Hybrid

$101K - $133K/yr

Proficiency in Python and/or TypeScript for API and AI integration development * Familiarity with ... Hands-on experience building LLM-powered applications, RAG pipelines, or agentic systems * Strong ...

Pwc

GenAI Python Systems Engineer-Manager

Atlanta, GA · On-site

$99K - $232K/yr

... Python and SQL - Experience with Docker and containerized deployments - Skilled in AI techniques ... LLM optimization - Implementing data integration solutions using AWS, Azure, GCP - Utilizing AWS ...

Insight Global

Solution Architect

Atlanta, GA

$60.50 - $79.75/hr

Your expertise with LLM enabling technologies such as Databricks and Python in a multi-cloud context effectively enables solution architecture. Join us in creating a dynamic, equitable, and ...

Tata Consultancy Service Limited

AI Engineer

Atlanta, GA · On-site

$120K - $140K/yr

Python technology + Additional backend knowledge * Hands on GCP experience (Vertex AI preferred ... Hands-on Experience AI/LLM application or chatbots * Domain knowledge Supply chain / transportation ...

RIT Solutions, Inc.

Senior AI Engineer

Atlanta, GA · On-site

$100K - $138K/yr

Responsibilities : • Architect and deliver end-to-end LLM-powered applications and agentic workflows using Python • Design and implement RAG pipelines over enterprise data using embeddings and ...

Insight Global

Solutions Architect DA

Atlanta, GA

$60.50 - $79.75/hr

Your expertise with LLM enabling technologies such as Databricks and Python in a multi-cloud context effectively enables solution architecture. Join us in creating a dynamic, equitable, and ...

CLPS Global

Senior AI Engineer

Alpharetta, GA · On-site

$119K - $157K/yr

Design, build, and deploy Python-based AI/LLM applications using FastAPI/Flask. * Develop REST APIs and scalable enterprise solutions. * Build AI-powered data analytics and self-service platforms.

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All JobsPython Llm JobsPython Llm Jobs in Atlanta, GA

Python Llm information

See Atlanta, GA salary details

$12

$56

$82

How much do python llm jobs pay per hour?

As of Aug 18, 2026, the average hourly pay for python llm in Atlanta, GA is $56.37, according to ZipRecruiter salary data. Most workers in this role earn between $46.44 and $64.04 per hour, depending on experience, location, and employer.

What is a Python LLM?

A Python LLM job involves working with Large Language Models (LLMs) using Python to develop, fine-tune, and deploy AI models. Responsibilities may include data preprocessing, prompt engineering, model optimization, and integration with applications. Professionals in this role often work with frameworks like TensorFlow, PyTorch, or Hugging Face Transformers. They may also contribute to improving model efficiency, reducing bias, and ensuring ethical AI usage.

What are the key skills and qualifications needed to thrive in the Python LLM position, and why are they important?

To excel as a Python LLM (Large Language Model) Engineer, you need strong skills in Python programming, machine learning, and natural language processing, typically supported by a degree in computer science or a related field. Proficiency with libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and experience with model deployment platforms are often essential, alongside certifications in AI or data science. Effective communication, problem-solving abilities, and collaboration are important soft skills for working in interdisciplinary teams and delivering results in dynamic environments. These skills ensure the development, fine-tuning, and deployment of advanced language models that meet both technical and business objectives.

What are some common challenges faced by Python LLM engineers in their daily work?

Python LLM Engineers often encounter challenges related to optimizing model performance, managing large datasets, and adapting models to specific business needs. Working with large-scale language models requires balancing computational resource limitations with the need for high accuracy and efficiency. Collaboration with data scientists, product managers, and DevOps engineers is routine to ensure seamless model integration and deployment. Staying updated on the latest advancements in NLP and continuously improving models based on user feedback are also important aspects of the role.

What are the most commonly searched types of Python Llm jobs in Atlanta, GA?

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Python Llm jobs near you
Infographic showing various Python Llm job openings in Atlanta, GA as of August 2026, with employment types broken down into 2% Internship, 85% Full Time, 6% Part Time, and 7% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $117,257 per year, or $56.4 per hour.

Senior Software Engineer - Agentic AI - Python Expert

Socket.dev

Atlanta, GA • On-site

$180 - $240/hr

Other

Posted 13 days ago

Job description

We are hiring senior engineers who build fast, think AI-first, and can take agentic AI from prototype to production. You will design, ship, and operate agentic systems that combine large language models (LLMs), tools/functions, planning, memory, evaluation, and multi-agent communication. You will work primarily in Python for AI services and integrate with our enterprise stack (TypeScript/Angular, .NET/C#, SQL Server, Azure), delivering trustworthy, cost-efficient, low-latency experiences in real customer workflows.

What You'll Do!
  • Build agentic AI applications on Azure AI Foundry: Azure OpenAI models, Prompt Flow, tools/function-calling, evaluations, vector search (Azure AI/Cognitive Search), and orchestration for multi-step reasoning and tool use.
  • Design memory & grounding: implement episodic/semantic/long-term memory with vector/graph stores; architect RAG pipelines and retrieval strategies that improve factuality and reduce latency/cost.
  • Integrate via Model Context Protocol (MCP) to standardize tool/skill access; design agent-to-agent communication, delegation, and event-driven workflows.
  • Connect agents to Microsoft Fabric (OneLake, Lakehouse, Warehouse, Real-Time Analytics) and Dataverse entities/workflows; ensure lineage, governance, and auditability.
  • Develop AI-native backend services in Python (FastAPI, asyncio) with evaluation harnesses, observability, and cost/latency/quality dashboards.
  • Embed AI features into the Speria stack: TypeScript/Angular UIs, .NET/C# services, SQL Server, NServiceBus, Azure DevOps pipelines, and Ionic/Cypress where applicable.
  • Use AI-augmented development tools like GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding workflows to accelerate delivery, test generation, refactoring, and documentation.
  • Implement safety & reliability: guardrails, red-teaming, PII protection, prompt hardening, regression tests, automated evaluations; uphold SLO/SLA excellence in production.
  • Implement full cycle agentic engineering: design → model/tool selection → API & UI → deployment → monitoring → continuous improvement.
What You Bring! Core AI & Agentic Expertise
  • Proven experience building LLM-powered applications with Azure OpenAI, embeddings, vector stores, RAG, prompt engineering, and evaluation pipelines.
  • Hands-on with agent frameworks such as Semantic Kernel, LangGraph, LangChain Agents, AutoGen, or CrewAI.
  • Ability to design deterministic, evaluatable, and safe agent behaviors including function schemas, tool success metrics, fallback strategies.
  • Practical use of Prompt Flow for authoring, testing, and deploying multi-step AI workflows in Azure AI Foundry.
MCP, Memory & Agentic Communication
  • Experience building and consuming MCP services to standardize tool access across agents.
  • Implemented memory architectures (episodic, semantic, vector, graph) and long-running conversational context.
  • Designed agent-to-agent communication patterns (messaging, orchestration, delegation, arbitration).
Microsoft Data & App Platform
  • Integration with Microsoft Fabric, SQL Server, Supabase, Databricks (OneLake/Lakehouse/Warehouse/Real-Time) for grounding data, retrieval, and telemetry.
  • Working knowledge of Dataverse entities, actions, and triggers; connecting agents to line-of-business records and Power Platform workflows.
  • Databricks for ELT, Delta Lake pipelines, feature engineering, ML training/serving, MLflow tracking and model lifecycle.
  • Azure IoT Hub/IoT Edge pipelines to incorporate device telemetry and edge-to-cloud intelligence into agentic workflows.
  • Azure services: App Service/Functions/AKS, Key Vault, Storage, Event Hubs/Service Bus, Monitor/Application Insights.
Python & Backend Engineering
  • Production-grade Python (FastAPI, asyncio, type hints), Postgres/SQL, Redis, queues, OpenTelemetry, CI/CD, and containerization.
  • Strong API design, testing (unit/integration/property-based), performance tuning, and reliability engineering.
Front-End & Speria Enterprise Stack
  • Experience in TypeScript/Angular for operator consoles and human-in-the-loop oversight.
  • Ability to integrate with .NET/C#, SQL Server, NServiceBus and Azure DevOps in our enterprise environment.
AI-Native Dev Workflow & Culture
  • Daily use of GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding to speed delivery and raise quality.
  • Mentor teams in prompting, agent behavior design, context management, evaluation, and AI-assisted engineering practices.
  • Seasoned aptitude for action, tight feedback loops, crisp written communication, and ownership mindset.
Success Looks Like (Outcomes)
  • Quality & reliability: rising agent tool-use success rate; falling hallucination/retry rates; low incident volume; fast MTTR.
  • Performance & cost: P50/P95 latency and token-cost budgets met; measurable efficiency gains across services.
  • Adoption & impact: shipped features used by real users; clear business KPIs improved via automation/intelligence.
  • Engineering excellence: high test coverage, stable CI/CD, observable systems, and healthy on-call posture.
Tooling & Stack Summary
  • AI & Agentic: Azure AI Foundry (Azure OpenAI, Prompt Flow, evaluations), MCP, Semantic Kernel, LangGraph, LangChain, AutoGen, CrewAI, HuggingFace embeddings, vector DBs, Azure AI/Cognitive Search, RAG, memory architectures.
  • Data & Integration: Databricks (ELT, ML, Delta Lake, MLflow), Microsoft Fabric (OneLake/Lakehouse/Warehouse/Real-Time), Dataverse, Event Hubs/Service Bus.
  • IoT: Azure IoT Hub, IoT Edge, stream ingestion & device telemetry flows.
  • Services: Python (FastAPI, asyncio), .NET/C#, REST/gRPC, containers, CI/CD with Azure DevOps.
  • Frontend: TypeScript/Angular, Ionic; E2E testing with Cypress.
  • AI-Native Dev Tools: GitHub Copilot, Bolt, Cursor, Replit, vibe-coding workflows.
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