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Google Cloud Machine Learning Engineer Jobs (NOW HIRING)

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Implement Machine Learning models (specifically leveraging BigQuery ML or Vertex AI) to identify ... Google Cloud Professional Data Engineer certification * Google Professional Machine Learning ...

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ... Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ...

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

As a Machine Learning Engineer, you will play a critical role in shaping the future of cooking ... Experience working with public cloud platforms such as AWS, Google Cloud Platform, or Microsoft ...

Sr. Lead Machine Learning Engineer

San Jose, CA · On-site +1

$120K - $158K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

Sr. Lead Machine Learning Engineer

Cambridge, MA · On-site +1

$112K - $147K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

Sr. Lead Machine Learning Engineer

New York, NY · On-site +1

$112K - $147K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

Sr. Lead Machine Learning Engineer

Mclean, VA · On-site +1

$103K - $136K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

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Google Cloud Machine Learning Engineer information

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$23

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$87

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

As of Jul 8, 2026, the average hourly pay for google cloud machine learning engineer in the United States is $62.89, according to ZipRecruiter salary data. Most workers in this role earn between $53.61 and $71.63 per hour, depending on experience, location, and employer.

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 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 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 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.
More about Google Cloud Machine Learning Engineer jobs
What cities are hiring for Google Cloud Machine Learning Engineer jobs? Cities with the most Google Cloud Machine Learning Engineer job openings:
What are the most commonly searched types of Google Cloud Machine Learning Engineer jobs? The most popular types of Google Cloud Machine Learning Engineer jobs are:
What states have the most Google Cloud Machine Learning Engineer jobs? States with the most job openings for Google Cloud Machine Learning Engineer jobs include:
Infographic showing various Google Cloud Machine Learning Engineer job openings in the United States as of July 2026, with employment types broken down into 93% Full Time, 3% Part Time, and 4% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $130,802 per year, or $62.9 per hour.

Google Cloud AI Solutions Architect, Gemini Enterprise

The Data Sherpas

Atlanta, GA

$61 - $83.75/hr

Full-time

Posted 22 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.