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Google Cloud Machine Learning Engineer Jobs in Ceres, CA

GCP Architect or Engineer - Fully Remote

Modesto, CA · On-site

$67.75 - $86.25/hr

S.) Reports To Trace3 Google Cloud Practice / Engagement leadership 43647 Role Overview Serves as ... Security Engineer, and/or Cloud Network Engineer). • Vertex AI / generative-AI platform ...

New

The System Engineer focuses on troubleshooting, maintaining, and stabilizing complex electro ... Applying next-gen technology, high-density storage and machine learning to solve today's complex ...

The System Engineer focuses on troubleshooting,maintaining, and stabilizing complex electro ... Applying next-gen technology, high-density storage and machine learning to solve today's complex ...

System Engineer

Lathrop, CA · On-site

$71 - $107/hr

The System Engineer focuses on troubleshooting,maintaining, and stabilizing complex electro ... Applying next-gen technology, high-density storage and machine learning to solve today's complex ...

Solutions Architect

Delhi, CA · On-site

$120 - $150/hr

Who we are DataHavn IT Solutions is a company that specializes in big data and cloud computing, artificial intelligence and machine learning, application development, and consulting services. The ...

Senior SAP Consultant

Modesto, CA · On-site

$150 - $190/hr

Experience with Agile or DevOps delivery methodologies. * Exposure to AI, SAP Business AI, SAP Joule, SAP BTP, machine learning, or enterprise automation initiatives. * SAP certifications are a plus.

Keep learning - Take advantage of tuition reimbursement to further your education or skillset ... Has knowledge of commonly used concepts, practices, and procedures utilized in machine/system ...

Leveraging our extensive expertise across multiple domains such as Cloud technology, Salesforce, AI, Machine Learning, and Technical Writing, we consistently exceed expectations in catering to a wide ...

Java Developer

Modesto, CA · On-site

$80K - $110K/yr

Cloud & Container Deployment: Package applications into Docker containers and deploy/manage them ... We always motivate our people to keep learning new technologies and also give them platform where ...

Senior Maintenance Manager

Atwater, CA · On-site

$120K - $130K/yr

... of machinery and equipment within a food manufacturing facility. Reporting directly to the ... Coordinate with production, engineering, and other departments to prioritize maintenance activities ...

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Google Cloud Machine Learning Engineer information

See Ceres, CA salary details

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$64

$89

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

As of Aug 23, 2026, the average hourly pay for google cloud machine learning engineer in Ceres, CA is $64.46, according to ZipRecruiter salary data. Most workers in this role earn between $54.95 and $73.41 per hour, depending on experience, location, and employer.

What is a Google Cloud Machine Learning engineer?

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?

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 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 popular job titles related to Google Cloud Machine Learning Engineer jobs in Ceres, CA?

For Google Cloud Machine Learning Engineer jobs in Ceres, CA, the most frequently searched job titles are:

What cities near Ceres, CA are hiring for Google Cloud Machine Learning Engineer jobs?

Cities near Ceres, CA with the most Google Cloud Machine Learning Engineer job openings:

GCP Architect or Engineer - Fully Remote

Calance

Modesto, CA • On-site

$67.75 - $86.25/hr

Other

Posted 3 days ago

New


Job description

Role GCP Enterprise Architect (Platform Automation Architect, GCP)


Practice Trace3 — Cloud Solutions Group, Digital · Google Cloud Practice


Workstream Product Teams Support


Location Remote (U.S.)


Reports To Trace3 Google Cloud Practice / Engagement leadership

43647

Role Overview

Serves as the dedicated enterprise architect for Gap's Google Cloud program, evaluating incoming use cases holistically and defining the most appropriate end-to-end solution design across security, networking, and infrastructure automation. This Trace3-led role translates business and technical requirements into scalable, secure, and supportable cloud automation patterns; produces documented, repeatable architecture artifacts; and coordinates required changes across dependent teams to completion. The architect strengthens the overall solution-design process, improves cross-functional alignment, and accelerates delivery of Gap's cloud initiatives.

Key Responsibilities

• Evaluate incoming use cases holistically and define end-to-end solution designs spanning security, networking, and infrastructure automation.

• Translate business and technical requirements into scalable, secure, and supportable cloud automation patterns.

• Produce documented, repeatable architecture artifacts and reference designs that delivery teams can operationalize.

• Partner across Gap's InfoSec, Network, and Infrastructure Automation teams to ensure designs align across all foundational pillars and can be operationalized effectively.

• Coordinate required changes across dependent teams and drive them to completion.

• Provide architectural direction across the program's technical domains — network security architecture, foundational GCP organization and IAM, cloud security posture management, Terraform/IaC structure, and the Gemini Enterprise Agent (Vertex AI) platform.

• Guide persona-based least-privilege IAM design, organization policy, VPC Service Controls, Private Service Connect, and CMEK/encryption standards.

• Establish Terraform architecture standards — separation of global/shared policy from perimeter-specific implementation, shared-module strategy, drift detection, and CI/CD policy-as-code gating.

• Provide architectural guidance to the NCC network transition and hybrid-connectivity direction (including Cross-Cloud Interconnect to Azure) where it intersects platform automation.

• Support project governance — participate in design reviews, secure architecture sign-off, and maintain alignment to timelines and scope.

Primary Deliverables

• Architecture designs and supporting documentation for approved use cases.

• Reusable reference solution patterns spanning security, networking, and infrastructure automation.

• Persona-based IAM / least-privilege role design and foundation policy recommendations.

• Terraform repository and structure design guidance (global vs. shared policy separation, module strategy, drift detection, IaC security scanning).

• Design artifacts for the Gemini Enterprise Agent Platform (Agent Engine, RAG Engine, Vector Search), including governance and threat-model considerations.

Required Qualifications

• Extensive enterprise / cloud architecture experience with deep, hands-on Google Cloud Platform expertise.

• Demonstrated ability to own end-to-end solution design across security, networking, and infrastructure automation.

• Expert-level Terraform / Infrastructure-as-Code design and standards.

• Strong command of GCP foundations: resource hierarchy and organization policy, IAM and least-privilege design, Shared VPC, VPC Service Controls, Private Service Connect, and CMEK.

• Experience embedding security and compliance into cloud designs (e.g., PII/PCI-regulated workloads) and cloud security posture management.

• Proven cross-functional leadership — aligning InfoSec, Network, and Infrastructure/Platform teams and driving change to completion.

• Excellent documentation, communication, and stakeholder-management skills.

Preferred Qualifications

• Google Cloud Professional certifications (Cloud Architect, Cloud Security Engineer, and/or Cloud Network Engineer).

• Vertex AI / generative-AI platform architecture — Agent Engine, RAG Engine, Vector Search, Model Armor, and Model Garden governance.

• CI/CD policy-as-code tooling (OPA/Conftest, Checkov, tfsec) and layered Terraform pipelines.

• Cloud security posture management with Prisma Cloud.

• Network Connectivity Center (NCC), hub-and-spoke fabric, and hybrid connectivity including Cross-Cloud Interconnect to Azure.

• Experience in large, regulated, enterprise-scale (e.g., retail) environments.

Core Technology Environment

Google Cloud Platform; Terraform / Infrastructure-as-Code; IAM, organization policy, Shared VPC, VPC Service Controls, Private Service Connect, and CMEK; Vertex AI / Gemini (Agent Engine, RAG Engine, Vector Search, Model Armor, Model Garden); Cloud Run; Prisma Cloud (CSPM); CI/CD policy-as-code (OPA/Conftest, Checkov, tfsec); and Network Connectivity Center with Cross-Cloud Interconnect to Azure.


Engagement Details & Working Arrangement

• Delivery model: Dedicated Trace3 consulting resource embedded with Gap's GCP program, supporting the Product Teams Support workstream alongside the broader GCP Architecture Support and NCC Transition projects.

• Location: Remote (U.S.). All work performed remotely via secure VPN/collaboration tooling.

• Hours: Normal business hours (8:00 AM – 5:00 PM client local time); occasional off-hours work by mutual agreement to support change windows.

• Security & compliance: Trace3-managed device, hard-drive encryption, two-factor authentication, and adherence to the client's remote-work protocols and information-security standards.