1

Generative Ai Certification Jobs in Georgia (NOW HIRING)

AWS Certified Solutions Architect (PLS UPLOAD CERTIFICATION) Experience with state and local Government is a plus. Experience with cutting-edge AI technologies in developing chatbots and generative ...

AI Java Full Stack Lead

Atlanta, GA · On-site

$70 - $75/hr

Design and implement AI-powered healthcare solutions using LLMs and Generative AI technologies ... AI, Cloud, or Java certifications preferred. Skills: * Strong experience in Java, Spring Boot ...

Drive the overarching enablement strategy for Generative AI (text, image, video, dynamic creative ... Curate external training resources and certification pathways to fast-track internal AI fluency and ...

Oversee the delivery of AI solutions, including predictive analytics, generative AI, Copilot ... PMP, Agile, Scrum, or related certifications. WHAT WE OFFER At Mativ, our benefits reflect how much ...

Define solution architectures that integrate LLMs, generative AI, and traditional ML with ... Professional certifications in cloud AI/ML platforms (for example: Azure AI Engineer Associate, AWS ...

Showing results 21-40

Generative Ai Certification information

What is a generative AI certification?

A Generative AI Certification is a credential that demonstrates your knowledge and skills in developing, deploying, and understanding generative artificial intelligence models, such as those used for creating text, images, audio, or video. These certifications typically cover foundational AI concepts, deep learning, neural networks, and hands-on experience with tools like GPT, DALL-E, or similar models. Earning a certification can enhance your credibility and job prospects in AI-related fields, proving your ability to work with cutting-edge generative technologies.

What are the key skills and qualifications needed to thrive as a generative AI specialist, and why are they important?

To thrive as a Generative AI Specialist, you need a solid background in computer science, machine learning, and deep learning, often supported by a relevant degree and experience with AI frameworks. Proficiency in tools like TensorFlow, PyTorch, and experience with generative models such as GANs or transformers, as well as certifications in AI, are typically required. Strong problem-solving, creativity, and effective communication skills help distinguish top performers in this field. These skills are essential for developing innovative AI solutions, collaborating with cross-functional teams, and addressing complex challenges in generative AI applications.

What are some common challenges professionals face when applying generative AI techniques in real-world projects?

Professionals working with generative AI often encounter challenges such as ensuring data quality, managing computational resource demands, and aligning model outputs with business objectives. Successfully deploying generative models requires close collaboration with data engineers, domain experts, and stakeholders to address ethical considerations and ensure outputs are both accurate and useful. Additionally, adapting quickly to the fast-evolving landscape of generative AI tools and frameworks is crucial for staying effective and competitive in the field.

What is the difference between Generative Ai Certification vs Data Scientist?

AspectGenerative Ai CertificationData Scientist
Required credentialsCertification programs, online coursesDegree in data science, statistics, or related fields
Work environmentTraining, project-based, AI-focused teamsResearch, analysis, data modeling in various industries
Employer usageTech companies, AI startups, training providersTech firms, finance, healthcare, consulting
Search intentLearning AI generative models, certification programsAnalyzing data, building models, insights

Generative Ai Certification focuses on training individuals in AI generative models through courses and certifications, often for skill validation. Data Scientists typically hold degrees and perform data analysis, modeling, and insights across industries. While both roles involve AI and data, certifications are more about skill validation, whereas data scientists are involved in comprehensive data analysis and modeling tasks.

Is generative AI certification worth it?

Generative AI certification can enhance a job seeker's credentials by validating skills in AI model development, data handling, and relevant tools like Python or TensorFlow. It can improve employability in roles such as AI engineer or data scientist, especially as demand for AI expertise grows. However, practical experience and project work are also important factors in securing related positions.

What jobs can you get with a generative AI certification?

A generative AI certification can qualify you for roles such as AI developer, machine learning engineer, data scientist, or AI researcher, where skills in neural networks, deep learning, and AI tools are essential. These positions often involve developing, training, and deploying AI models in various industries like technology, healthcare, and finance.

What are the most commonly searched types of Generative Ai Certification jobs in Georgia?

The most popular types of Generative Ai Certification jobs in Georgia are:

What job categories do people searching Generative Ai Certification jobs in Georgia look for?

The top searched job categories for Generative Ai Certification jobs in Georgia are:

What cities in Georgia are hiring for Generative Ai Certification jobs?

Cities in Georgia with the most Generative Ai Certification job openings:

Infographic showing various Generative Ai Certification job openings in Georgia as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Google Cloud AI Solutions Architect, Gemini Enterprise

The Data Sherpas

Atlanta, GA • On-site

$61 - $83.75/hr

Full-time

Re-posted 23 days ago


Job description

Google Cloud AI Solutions Architect, Gemini Enterprise
Overview
We are seeking a hands-on Google Cloud AI Solutions Architect to design, build, configure, and implement Gemini Enterprise and agentic AI solutions for end clients. This is a client-facing technical delivery role focused on applied AI/ML implementation, not sales.
The right candidate will have strong Google Cloud experience, hands-on Gemini Enterprise or Google Cloud generative AI implementation experience, and the ability to translate client workflows into secure, scalable, production-ready AI solutions. This person should be comfortable moving between architecture, coding, prototyping, configuration, integration, and client-facing technical delivery.
Responsibilities
  • Design, build, configure, and implement Gemini Enterprise solutions for end clients.
  • Develop AI agent workflows that support business use cases, internal processes, enterprise automation, and operational workflows.
  • Build prototypes and proofs of concept that can be iterated into production-ready solutions.
  • Design and implement applied AI/ML solutions using Gemini Enterprise, Vertex AI, and related Google Cloud AI services.
  • Build and deploy LLM-powered applications, AI agents, retrieval-augmented generation workflows, and enterprise AI integrations.
  • Evaluate model options, agent patterns, grounding strategies, retrieval approaches, and integration paths based on client use cases.
  • Configure and deploy Gemini Enterprise agents, integrations, and related Google Cloud AI services.
  • Integrate AI agents with enterprise systems, data sources, APIs, and business applications.
  • Lead technical discovery with clients and translate requirements into solution architecture and implementation plans.
  • Develop scripts, connectors, workflows, or lightweight applications needed to support AI agent implementation.
  • Support model evaluation, prompt optimization, testing, validation, troubleshooting, and production readiness.
  • Apply best practices for cloud security, IAM, data governance, responsible AI, monitoring, and enterprise deployment.
  • Communicate technical recommendations clearly to client engineering, data, security, cloud, and business stakeholders.

Qualifications
  • Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, Machine Learning, or a related field; equivalent practical experience will also be considered.
  • 5+ years of experience in cloud architecture, AI/ML solution architecture, technical consulting, solution architecture, software engineering, or hands-on client-facing technical delivery.
  • 3+ years of experience working with Google Cloud Platform.
  • Google Cloud Professional Cloud Architect or Google Cloud Professional Machine Learning Engineer certification.
  • Hands-on experience implementing Gemini Enterprise or Google Cloud generative AI solutions.
  • Hands-on experience designing or implementing AI/ML solutions using Google Cloud AI services, including Vertex AI, Gemini, Gemini Enterprise, Agent Builder, Agent Development Kit, or related tools.
  • Experience building, configuring, deploying, or integrating AI agents, generative AI applications, LLM-powered applications, or enterprise AI workflows.
  • Experience building agentic AI workflows using Google Cloud Agent Development Kit, Vertex AI Agent Engine, Agent Builder, or related agent development tools.
  • Experience with core agentic AI implementation patterns such as retrieval-augmented generation, prompt engineering, tool use/function calling, API integrations, enterprise system integration, and/or multi-agent workflows.
  • Experience with LLM application development, embeddings, model evaluation, prompt optimization, and production AI/ML implementation patterns.
  • Strong understanding of Google Cloud AI and data services, such as Vertex AI, Gemini, Gemini Enterprise, BigQuery, BigQuery ML, Cloud Functions, Cloud Run, APIs, IAM, and related services.
  • Ability to code, script, prototype, and troubleshoot technical solutions in client environments.
  • Experience working directly with enterprise clients or internal business stakeholders to gather requirements and implement technical solutions.
  • Strong understanding of cloud security, IAM, data governance, responsible AI, and enterprise deployment best practices.
  • Excellent communication skills with the ability to explain complex technical concepts clearly.
  • Must be a U.S. Citizen.

Preferred Skills
  • Google Cloud Generative AI Leader certification.
  • Experience as a Forward Deployed Engineer, Solutions Architect, AI Architect, ML Engineer, Customer Engineer, Technical Consultant, or hands-on implementation architect.
  • Experience with Python, JavaScript, TypeScript, or similar programming languages.
  • Experience with data integration, workflow automation, enterprise applications, embeddings, vector search, semantic search, model grounding, enterprise search, or retrieval-augmented generation pipelines.
  • Experience in consulting, systems integration, professional services, or client-facing technical delivery.
  • Familiarity with infrastructure as code, CI/CD, containers, serverless architecture, and cloud-native application deployment.

Additional Information
This position is open to direct candidates only. We are not working with third-party agencies, subcontractors, or C2C arrangements for this role.
Candidates must be U.S. Citizens.