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Remote Google Cloud Machine Learning Engineer Jobs in Chicago, IL

YO AI Labs

AI/ML Engineer - Remote

Chicago, IL · Remote

$200 - $350/hr

... and cloud AI platforms to develop production-grade machine learning applications. Key ... Deploy AI solutions across secure cloud environments such as AWS GovCloud, Google GovCloud, Azure ...

Ulta Beauty, Inc.

Sr Data Engineer (Remote)

Bolingbrook, IL · Remote

$102K - $140K/yr

... machine learning and agentic AI solutions on our GCP and Databricks platforms to enhance Ulta guest ... platforms like Google Cloud Platform and Databricks. * Build data engineering solutions on GCP ...

Ulta Beauty

Sr Data Engineer (Remote)

Bolingbrook, IL · On-site +1

$102K - $140K/yr

... machine learning and agentic AI solutions on our GCP and Databricks platforms to enhance Ulta guest ... platforms like Google Cloud Platform and Databricks. * Build data engineering solutions on GCP ...

Ulta Beauty, Inc.

Sr Data Engineer (Remote)

Bolingbrook, IL · Remote

$102K - $140K/yr

... machine learning and agentic AI solutions on our GCP and Databricks platforms to enhance Ulta guest ... platforms like Google Cloud Platform and Databricks. * Build data engineering solutions on GCP ...

Ontrac Solutions

GCP Cloud Platform Engineer

Chicago, IL · On-site +1

$60 - $80/hr

This role is ideal for a hands-on engineer with strong migration experience who can work independently to build, manage, and optimize production-ready infrastructure in Google Cloud Platform. The ...

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Remote Google Cloud Machine Learning Engineer information

See Chicago, IL salary details

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$64

$89

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

As of Sep 7, 2026, the average hourly pay for remote google cloud machine learning engineer in Chicago, IL is $64.78, according to ZipRecruiter salary data. Most workers in this role earn between $55.24 and $73.80 per hour, depending on experience, location, and employer.

What does a remote Google Cloud machine learning engineer do?

A Remote Google Cloud Machine Learning Engineer designs, develops, and deploys machine learning models on Google Cloud Platform (GCP) from a remote location. They work with cloud-based tools and services such as TensorFlow, Vertex AI, BigQuery, and Dataflow to build scalable, production-ready ML solutions. Their responsibilities also include data preprocessing, model training and evaluation, and integrating ML solutions with other cloud services. Collaboration with data scientists, software engineers, and stakeholders is a key part of the role, ensuring that ML solutions meet business goals while leveraging the full capabilities of Google Cloud.

How does a remote Google Cloud machine learning engineer typically collaborate with cross-functional teams?

As a Remote Google Cloud Machine Learning Engineer, collaboration often happens through virtual meetings, shared documentation, and cloud-based development environments. You'll regularly interact with data scientists, software developers, and product managers to align machine learning solutions with business objectives. Clear communication and proactive updates are essential, as you may work across time zones and need to coordinate on project requirements, data pipelines, and model deployment strategies. Tools such as Google Meet, Slack, and shared code repositories like Git are commonly used to facilitate seamless teamwork.

What are the key skills and qualifications needed to thrive as a remote Google Cloud machine learning engineer, and why are they important?

To thrive as a Remote Google Cloud Machine Learning Engineer, you need expertise in machine learning algorithms, data analysis, and proficiency in programming languages like Python, along with a degree in computer science or a related field. Familiarity with Google Cloud Platform (GCP) services such as Vertex AI, BigQuery, and TensorFlow, as well as relevant certifications like Google Professional Machine Learning Engineer, is highly valued. Strong problem-solving skills, self-motivation, and effective remote communication set top performers apart in this role. These competencies are critical for building scalable ML solutions, collaborating remotely, and delivering impactful results using cloud technologies.

What is the difference between Remote Google Cloud Machine Learning Engineer vs Remote AWS Machine Learning Engineer?

AspectRemote Google Cloud Machine Learning EngineerRemote AWS Machine Learning Engineer
Required CredentialsGoogle Cloud certifications, Python, ML frameworksAWS certifications, Python, ML frameworks
Work EnvironmentGoogle Cloud Platform, GCP toolsAWS Cloud, AWS tools
Industry UsageTech, finance, healthcare using GCPTech, retail, finance using AWS
Search & Comparison IntentHigh overlap in cloud-based ML rolesSimilar roles in cloud ML, different platform

Both roles involve developing machine learning models in cloud environments, requiring cloud platform certifications and expertise in Python and ML frameworks. The main difference lies in the cloud platform used: Google Cloud vs AWS. Candidates should choose based on their platform familiarity and employer requirements.

What are the most commonly searched types of Google Cloud Machine Learning Engineer jobs in Chicago, IL?

The most popular types of Google Cloud Machine Learning Engineer jobs in Chicago, IL are:

What cities near Chicago, IL are hiring for Remote Google Cloud Machine Learning Engineer jobs?

Cities near Chicago, IL with the most Remote Google Cloud Machine Learning Engineer job openings:

Remote Google Cloud Machine Learning Engineer jobs near you

AI/ML Engineer - Remote

YO AI Labs

Chicago, IL • Remote

$200 - $350/hr

Full-time

Posted 11 days ago

Job description

AI/ML Engineer

Job Type: Full-Time
Location: Remote

Job Summary

We are seeking an experienced AI/ML Engineer to build and deploy secure, scalable AI solutions for mission-critical initiatives while contributing to proprietary AI infrastructure. You will work with LLMs, RAG, prompt engineering, multi-agent systems, and cloud AI platforms to develop production-grade machine learning applications.

Key Responsibilities
  • Design, implement, and optimize AI/ML solutions using LLMs, RAG, and prompt engineering.
  • Develop and orchestrate multi-agent systems using LangGraph and LangChain.
  • Deploy AI solutions across secure cloud environments such as AWS GovCloud, Google GovCloud, Azure IL5+, Vertex AI, and AWS Bedrock.
  • Build robust ETL and data pipelines, metadata catalogs, and ontologies for AI training and inference.
  • Develop and maintain REST APIs and SDK integrations.
  • Collaborate with product, security, and engineering teams to deliver secure, scalable solutions.
  • Follow modern secure coding, DevOps, and CI/CD practices.
  • Document technical decisions and communicate complex concepts effectively to technical and non-technical stakeholders.
Required Skills & Qualifications
  • Strong Python proficiency for AI/ML development, including REST APIs and SDK integrations.
  • Hands-on production experience with LLMs, RAG, and prompt engineering.
  • Experience with multi-agent orchestration, tool use, LangGraph, and LangChain.
  • Strong knowledge of cloud AI services, including AWS GovCloud, Google GovCloud, Azure IL5+, Vertex AI, and AWS Bedrock.
  • Experience building data pipelines, ETL processes, metadata catalogs, and ontologies.
  • Strong understanding of secure coding and CI/CD practices.
  • Excellent written and verbal communication skills.
Preferred Qualifications
  • Experience with enterprise AI platforms such as Anthropic for Gov, OpenAI Enterprise, Gemini Enterprise, or Grok Enterprise.
  • Knowledge of MCP, metadata catalog platforms, and advanced API development.
  • Experience working in government, regulated, or security-sensitive cloud environments.
  • Familiarity with relevant compliance and security standards.