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Google Cloud Machine Learning Engineer Jobs in Georgia

TheTalentos, LLC

Machine Learning Engineer

Atlanta, GA · On-site

Machine Learning Engineer Atlanta, Georgia, United States About the Job We are supporting a client ... Demonstrated experience deploying models and applications to a cloud environment using tools like ...

Tech Tammina

Google Cloud Lead

Atlanta, GA · On-site

... engineers to define requirements and design solutions. • Influence the team by providing ... Nice To Have: • Experience with machine learning technologies on Google like Prediction API. • ...

FanDuel

Senior Machine Learning Engineer

Atlanta, GA

$100.50K - $138K/yr

As a Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast ... Experience working in a cloud environment such as AWS, Google Cloud Platform, Azure. * Experience ...

Omnissa

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$162.51K - $342.75K/yr

Staff Machine Learning Engineer We are Omnissa. The world is evolving quickly, and organizations ... You'll work closely with engineering and product teams to operationalize models across our cloud ...

CNN

Machine Learning Engineer I

Atlanta, GA · On-site

CNN is a global leader in news and information, seeking a Machine Learning Engineer I to build and ... of cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes) • ...

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How much do google cloud machine learning engineer jobs pay per hour?

As of May 28, 2026, the average hourly pay for google cloud machine learning engineer in Georgia is $53.10, according to ZipRecruiter salary data. Most workers in this role earn between $45.24 and $60.48 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Google Cloud Machine Learning Engineer, and why are they important?

To thrive as a Google Cloud Machine Learning Engineer, you need strong programming skills in Python or Java, a deep understanding of machine learning algorithms, and a degree in computer science or a related field. Familiarity with Google Cloud Platform (GCP) services such as Vertex AI, BigQuery, TensorFlow, and relevant certifications like the Professional Machine Learning Engineer certification is highly valuable. Excellent problem-solving abilities, collaboration, and clear communication make someone stand out in this position. These skills and qualities are critical for designing, deploying, and optimizing scalable ML solutions that meet business objectives in cloud environments.

What are some typical cross-functional collaborations for a Google Cloud Machine Learning Engineer?

As a Google Cloud Machine Learning Engineer, you'll frequently work alongside data scientists, software engineers, and product managers to design, deploy, and maintain machine learning solutions at scale. Collaboration often involves translating business requirements into machine learning pipelines, integrating models into cloud-based applications, and ensuring that solutions are robust, secure, and scalable. Regular communication with DevOps and infrastructure teams is also common to optimize model deployment and monitor performance. This cross-disciplinary teamwork is crucial for delivering impactful, production-ready AI solutions.

What are Google Cloud Machine Learning Engineers?

Google Cloud Machine Learning Engineers are professionals who design, build, and deploy machine learning models using Google Cloud Platform (GCP) services and tools. They work with large datasets, develop scalable ML solutions, and collaborate with data scientists and software engineers. Their role often includes automating data pipelines, optimizing model performance, and ensuring the reliability and security of ML deployments on the cloud. These engineers have expertise in both machine learning algorithms and cloud infrastructure, making them key contributors to data-driven projects.

What is the difference between Google Cloud Machine Learning Engineer vs Data Scientist?

AspectGoogle Cloud Machine Learning EngineerData Scientist
Required CredentialsGoogle Cloud certifications, programming skills, ML knowledgeStatistics, data analysis, programming, often with advanced degrees
Work EnvironmentCloud platforms, coding, deploying ML modelsData analysis, modeling, reporting, often in research or business settings
Employer & Industry UsageTech companies, cloud service providers, enterprises using Google CloudVarious industries including finance, healthcare, marketing, research

Google Cloud Machine Learning Engineers focus on developing and deploying ML models on Google Cloud, requiring cloud certifications and coding skills. Data Scientists analyze data, build models, and generate insights, often with advanced degrees. While both roles work with data and ML, the Engineer role emphasizes cloud deployment and infrastructure, whereas Data Scientists focus on data analysis and modeling.

What are the most commonly searched types of Google Cloud Machine Learning Engineer jobs in Georgia? The most popular types of Google Cloud Machine Learning Engineer jobs in Georgia are:
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Google Cloud Machine Learning Engineer jobs near you

Machine Learning Engineer 3 4P/392

4P Consulting Inc

Atlanta, GA • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Machine Learning Engineer 3 (AI Engineer)
Location: Atlanta, GA
Client- Southern Comapny Gas
Contract- 1 Year
Job Summary
We are seeking a highly skilled Machine Learning Engineer (Level 3) with 5-10 years of experience to design, develop, and deploy advanced AI models and systems. This role requires expertise in machine learning, data analysis, and model deployment to optimize business operations and drive innovation within the utilities and energy sector.
The successful candidate will collaborate with cross-functional teams-including data scientists, engineers, and business stakeholders-to integrate AI solutions into real-world applications that support operational efficiency, customer service, and sustainability initiatives.
Key Responsibilities
  • AI Model Development: Design and implement machine learning models and algorithms to address utility-specific challenges such as grid optimization, asset reliability, predictive maintenance, and customer analytics.
  • Data Analysis: Analyze large, complex datasets from SCADA, AMI, and IoT systems to extract actionable insights.
  • Model Training & Evaluation: Train, test, and validate AI models to ensure accuracy, scalability, and compliance with industry reliability standards.
  • Deployment & Integration: Deploy AI solutions into production systems and integrate with enterprise platforms (e.g., Azure, Maximo, EMS/DMS systems).
  • Innovation: Stay current with the latest advancements in AI/ML and recommend solutions that can enhance grid resilience, safety, and efficiency.
  • Collaboration: Partner with engineering, IT, and business units to define requirements and deliver business-aligned AI solutions.
  • Performance Monitoring: Continuously monitor AI models and refine as needed to maintain performance and compliance.
  • Documentation & Knowledge Sharing: Create clear documentation of models, workflows, and processes for reuse and compliance.
Qualifications
Education:
  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.
Experience:
  • 5-10 years of experience in AI, ML, or data science roles, with proven success in AI model development and deployment.
  • Industry experience in utilities, energy, or large-scale infrastructure data is preferred.
Technical Skills:
  • Proficiency in Python, R, or Java.
  • Experience with ML frameworks: TensorFlow, PyTorch, scikit-learn.
  • Strong grasp of data structures, algorithms, and applied statistics.
  • Familiarity with cloud platforms such as Azure ML and Azure Databricks (preferred), AWS or Google Cloud (a plus).
  • Experience with big data tools (e.g., Spark, Hadoop) is desirable.
  • Exposure to natural language processing (NLP) or computer vision a plus.
Soft Skills:
  • Strong analytical and problem-solving abilities.
  • Excellent communication skills for cross-functional collaboration.
  • Ability to work independently and manage multiple projects simultaneously.
  • Experience working in agile or iterative development environments.
Preferred Qualifications
  • Lighting up AI/ML use cases in the utility/energy sector (e.g., outage prediction, DERMS optimization, vegetation management analytics).
  • Certifications in AI/ML, data science, or cloud platforms (Azure, AWS, GCP).
  • Experience with MLOps pipelines and CI/CD integration for model deployment.

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