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

Fint Solutions Inc

AI Engineer

Atlanta, GA · On-site

Develop MCP servers, gateways, and integrations with GitHub, Jira, Azure DevOps, and enterprise tools. * Deploy scalable AI solutions on Azure, AWS, or Google Cloud Platform. Required Skills:

New

Cyber Infrastructure (P) Limited

AI Engineer

Atlanta, GA · On-site

$120 - $160/hr

Role description\ AI Engineer designs and develops artificial intelligence models and algorithms ... Experience with cloud platforms (AWS, GCP, Azure) for deploying AI models. Knowledge of MLOps tools ...

New

ITBMS Inc.

AI Engineer

Alpharetta, GA · On-site

... s AWS/Azure, Docker, Kubernetes, CI/CD, GitHub Actions, Azure DevOps, Automated Testing, Secure SDLC, DevSecOps, Release Automation. Experience building enterprise AI platforms. Experience ...

Saxon Global

AI Customer Enginee

Atlanta, GA · On-site

We are looking for a Senior AI Customer Engineer with deep Cloud & AI Data Platform. Candidates will be supporting customers in building secure, scalable, and AI-ready solutions on Microsoft Azure.

KPMG US

Senior Associate, AI Engineer

Atlanta, GA · On-site

$53.25 - $68.50/hr

... Azure AI, AWS AI/Bedrock, or Google Cloud Vertex AI) • Proficiency in Python or another ... programming language commonly used in AI/ML • Familiarity with conversational AI frameworks (LLM ...

Genpact

AI Engineer 4A

Atlanta, GA · On-site

Familiarity with cloud platforms such as AWS or Azure. * Basic knowledge of databases and Linux environments. Qualifications Certifications Required Skills Artificial Intelligence (AI), Azure Devops, ...

Kaleris

AI Engineer Student Advisor

Alpharetta, GA · On-site

Solid programming skills in Python preferred, or Java or C++ * Familiarity with AI and Machine ... Familiarity with Microsoft Azure services including Azure AI Studio, Azure Functions, Logic Apps ...

Kaleris

AI Engineer Student Advisor

Alpharetta, GA · On-site

Solid programming skills in Python preferred, or Java or C++ * Familiarity with AI and Machine ... Familiarity with Microsoft Azure services including Azure AI Studio, Azure Functions, Logic Apps ...

Deloitte

AI Engineer Consultant

Atlanta, GA · Hybrid

... on AWS/Azure/GCP (one or more), including managed data platforms and scalable compute patterns ... We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ...

Deloitte

AI Engineer Consultant

Atlanta, GA · On-site

... on AWS/Azure/GCP (one or more), including managed data platforms and scalable compute patterns ... Professional development From entry-level employees to senior leaders, we believe there's always ...

Finastra

Senior AI Engineer

Atlanta, GA · On-site

$100K - $138K/yr

Deploy and manage LLM solutions on Azure OpenAI, AWS Bedrock, or similar cloud platforms ... AI Engineering Techniques: Knowledge of prompt engineering, context engineering, AI agent patterns ...

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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:

What job categories do people searching Entry Level Azure Ai Engineer jobs in Georgia look for?

The top searched job categories for Entry Level Azure Ai Engineer jobs in Georgia are:

What cities in Georgia are hiring for Entry Level Azure Ai Engineer jobs?

Cities in Georgia with the most Entry Level Azure Ai Engineer job openings:

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.

Machine Learning Engineer III / AI-ML Engineer

4pconsultinginc

Atlanta, GA • On-site

Contractor

Re-posted 17 days ago

Job description

Position:           Machine Learning Engineer III – AI/ML Engineer

Location:          Atlanta, GA

Duration:          6 Months

Client:             Southern Company services

Job Summary

We are seeking an experienced Machine Learning Engineer III / AI-ML Engineer to support the development of reusable, scalable AI products that can be deployed across multiple operating companies and business units.

This role will focus on building production-grade AI solutions, including Retrieval-Augmented Generation (RAG), multi-agent orchestration, NLP pipelines, transcription solutions, model deployment, and reusable AI components for internal operational workflows.

The ideal candidate will have strong hands-on experience with GCP or Azure AI services, modern ML frameworks, strong software engineering skills, and a product-focused mindset.

Key Responsibilities

  • Design and build modular, reusable AI components that can scale across business units.
  • Lead development of scalable RAG-based solutions for document comparison and analysis.
  • Work with structured and unstructured data to support AI-driven business solutions.
  • Engineer multi-agent systems for intelligent task coordination and decision support.
  • Develop transcription and NLP pipelines for customer interaction analysis.
  • Build, fine-tune, and deploy models using tools and frameworks such as PyTorch, Transformers, and LangChain.
  • Package models for deployment in GCP, Azure ML, and/or Databricks.
  • Integrate with Databricks for data ingestion, feature engineering, experimentation, and model development.
  • Work closely with MLOps, DevOps, and Data Engineering teams to align infrastructure and deployment patterns.
  • Contribute to shared libraries, APIs, templates, and reusable frameworks that accelerate AI product delivery.
  • Provide technical guidance to teams adopting reusable AI components.
  • Ensure AI products meet enterprise-grade security, compliance, scalability, and maintainability standards.
  • Implement monitoring for model performance, data drift, usage metrics, and production reliability.

Required Qualifications

  • Experience as a Machine Learning Engineer, AI Engineer, Data Scientist, or similar technical role.
  • Strong experience building production-grade AI/ML solutions.
  • Hands-on experience with cloud-based AI services, preferably GCP or Azure.
  • Experience developing RAG-based applications using structured and unstructured data.
  • Strong knowledge of machine learning, NLP, LLMs, and modern AI application patterns.
  • Experience with frameworks and tools such as:
    • PyTorch
    • Transformers
    • LangChain
  • Experience deploying models in cloud or enterprise environments.
  • Strong programming and software engineering skills.
  • Ability to work with APIs, reusable components, and scalable architectures.
  • Experience collaborating with MLOps, DevOps, and data engineering teams.
  • Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

  • Experience with Azure ML, GCP Vertex AI, Databricks, or similar platforms.
  • Experience designing multi-agent systems or AI orchestration workflows.
  • Experience developing transcription, NLP, or customer interaction analytics pipelines.
  • Experience with model monitoring, data drift detection, observability, and usage metrics.
  • Experience building shared AI libraries, reusable templates, or internal AI platforms.
  • Understanding of enterprise security, compliance, and governance requirements for AI products.
  • Product mindset with the ability to design AI solutions that are reusable, scalable, and business-focused.