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Entry Level Azure Ai Engineer Jobs in Georgia (NOW HIRING)

Worldwide TechServices

Azure Engineer

Atlanta, GA · On-site

Certifications: Entry-level certifications such as CompTIA Security+ or AWS Certified Cloud ... Basic understanding or interest in DevOps practices, including CI/CD pipelines and infrastructure ...

Worldwide TechServices

Azure Engineer

Atlanta, GA · On-site

Certifications: Entry-level certifications such as CompTIA Security+ or AWS Certified Cloud ... Basic understanding or interest in DevOps practices, including CI/CD pipelines and infrastructure ...

2T Consulting

We are seeking an AI & Microsoft Fabric Engineer to design and develop enterprise AI solutions using Agentic AI, Generative AI, Azure OpenAI, RAG, and Microsoft Fabric. The role focuses on building ...

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Entry Level Azure Ai Engineer information

What does an entry level Azure AI engineer do?

An Entry Level Azure AI Engineer is responsible for developing, deploying, and maintaining artificial intelligence solutions using Microsoft Azure's AI services and tools. They typically work on tasks such as building machine learning models, integrating AI APIs, and supporting cloud-based AI applications under the guidance of more experienced engineers. Their role often includes data preparation, testing AI solutions, and ensuring that AI models run efficiently and securely on Azure infrastructure. This position is ideal for those with foundational knowledge in AI, programming, and cloud computing who are looking to grow their skills in a real-world environment. Entry level engineers often collaborate with data scientists, developers, and project managers to deliver AI-driven solutions for various business needs.

What are the key skills and qualifications needed to thrive as an entry level Azure AI engineer?

To thrive as an Entry Level Azure AI Engineer, you need foundational experience in programming (such as Python or C#), understanding of machine learning concepts, and a relevant degree in computer science or a related field. Familiarity with Microsoft Azure services (like Azure Machine Learning, Cognitive Services), and certifications such as Microsoft Certified: Azure AI Engineer Associate are highly valued. Problem-solving skills, attention to detail, and effective communication help you collaborate with teams and explain AI solutions to stakeholders. These abilities ensure you can build, deploy, and maintain AI solutions efficiently in cloud-based environments, meeting business needs.

What are some common challenges faced by entry level Azure AI engineers when transitioning from academic projects to real-world applications?

Entry Level Azure AI Engineers often find that real-world projects involve integrating AI models with existing business systems and handling larger, messier datasets than those used in academic settings. Challenges may include learning to work with Azure-specific tools, collaborating with cross-functional teams, and meeting production-level reliability and scalability requirements. New hires also need to quickly adapt to agile development cycles and ongoing feedback, which can differ significantly from the more controlled pace of academic projects.

What is the difference between Entry Level Azure Ai Engineer vs Data Scientist?

AspectEntry Level Azure Ai EngineerData Scientist
Required CredentialsAzure certifications, basic programming skillsStatistics, programming, often a master's degree
Work EnvironmentCloud platforms, AI development teamsData analysis, research, modeling
Industry UsageTech, cloud services, AI projectsFinance, healthcare, tech, research

While both roles involve working with data and AI, an Entry Level Azure Ai Engineer focuses on deploying AI solutions on Azure cloud platforms, requiring cloud certifications and programming skills. A Data Scientist primarily analyzes data, builds models, and may not require cloud-specific certifications. The roles overlap in data handling but differ in technical focus and work environment.

What are the most commonly searched types of Azure Ai Engineer jobs in Georgia?

The most popular types of Azure Ai Engineer jobs in Georgia are:

What are popular job titles related to Entry Level Azure Ai Engineer jobs in Georgia?

For Entry Level Azure Ai Engineer jobs in Georgia, the most frequently searched job titles are:

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Entry Level Azure Ai Engineer jobs near you
Infographic showing various Entry Level Azure Ai Engineer job openings in Georgia as of August 2026, with employment types broken down into 71% Full Time, 19% Part Time, 7% Contract, and 3% Nights. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution.

Agentic AI Engineer (LLM Orchestration & Multi-Agent Systems) - Q3-2026

R2 Technologies Corporation

Alpharetta, GA • On-site

Full-time

Posted 10 days ago

Job description

Overview:
About R2 Technologies: R2 Technologies is a Certified Minority Business Enterprise (MBE) headquartered in Alpharetta, GA. With over two decades of experience across global markets, we provide IT staffing and digital product engineering services to clients ranging from startups to Fortune 1000 companies. In addition to talent services, R2 develops proprietary solutions including SmartEnt, an enterprise AI and IoT intelligence platform. We work closely with our clients to deliver technology outcomes that are realistic, measurable, and impactful.
Job Summary: The chatbot era is over-enterprises now want software that acts, not just answers. R2 Technologies is seeking an Agentic AI Engineer to design and ship production AI agents that plan, reason, call tools, and complete multi-step business workflows end to end. You will work across the full lifecycle of an agent-orchestration design, tool integration, memory and context management, evaluation, and production deployment-building systems that power enterprise client applications and our own SmartEnt platform.
Key Responsibilities:
  • Design and build production AI agents and multi-agent orchestration workflows using LangGraph, LangChain, CrewAI, or the Claude Agent SDK.
  • Implement agent reasoning, planning, tool/function calling, and memory and state management under real token, latency, and cost constraints.
  • Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models to ground agents in enterprise data.
  • Integrate Large Language Models-Anthropic Claude, OpenAI GPT, Google Gemini-into enterprise applications via AWS Bedrock, Azure OpenAI, or direct API and MCP-based tool integration.
  • Develop programmatic agent evaluations, including offline and online metrics, trajectory scoring, and failure-mode analysis to move agents from demo to dependable.
  • Implement guardrails, tracing, and observability to ensure agent decisions are auditable and compliant with enterprise security and governance standards.

Qualifications:
  • 3 years of experience in AI/ML Engineering, Backend Engineering, or applied LLM development.
  • Strong hands-on proficiency in Python, with working knowledge of Java, TypeScript, or Go.
  • Hands-on experience with agentic frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Google ADK, or the Claude Agent SDK.
  • Proven experience building RAG architectures with vector databases (Pinecone, Weaviate, ChromaDB, FAISS, or Milvus).
  • Strong understanding of prompt engineering, context engineering, embeddings, and model evaluation.
  • Experience deploying containerized services on AWS, Azure, or GCP using Docker, Kubernetes, and CI/CD pipelines.

Skills:
Agentic AI, LangGraph, LangChain, Python, RAG, MCP, Claude, OpenAI, Vector Databases, AWS Bedrock, Multi-Agent Systems
Skills:
Agentic AI,LangGraph,LangChain,Python,RAG,Multi-Agent Systems,LLM Integration,Vector Databases,Prompt Engineering

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