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Contract Google Rf Engineer Jobs in Chicago, IL (NOW HIRING)

Co-Sourcing Partners

GCP AI/ML Engineer

Chicago, IL · On-site

Job Title: GCP AI/ML Engineer Duration: 6 months Contract to hire Location: Chicago is the ... Google Cloud Platform (GCP). This role focuses on developing production-grade ML pipelines ...

PGS Worldwide

Software Engineer

Rolling Meadows, IL

$141.30/hr

PGS Worldwide is seeking a Software Engineer in Rolling Meadows, IL for a one-year contract role ... Experience with RF systems, engineering applications, manufacturing software, Agile development ...

CoSourcing Partners

GCP Data Engineer

Chicago, IL · On-site

$118K - $141K/yr

GCP Data Engineer Duration: 6 months Contract to hire Location: Chicago is the preferred location ... Google Cloud Platform (GCP). The ideal candidate will have strong expertise in building robust ...

Co-Sourcing Partners

GCP Data Engineer

Chicago, IL · On-site

$118K - $141K/yr

GCP Data Engineer Duration: 6 months Contract to hire Location: Chicago is the preferred location ... Google Cloud Platform (GCP). The ideal candidate will have strong expertise in building robust ...

Motorola

Principal Systems Engineer

Schaumburg, IL

$130K - $170K/yr

... government contracts Additional preferred skills: * Experience with development of wireless ... Experience with RF, Digital Hardware, FPGA, and Mechanical design * Embedded OS systems (Android ...

Chipton Ross

Software Engineer 3

Rolling Meadows, IL · On-site

$57.75 - $77.75/hr

Job #218473 Chipton-Ross is seeking a Software Engineer 3 for a contract opportunity in Rolling ... the RF Business Unit. * This includes interfacing with customers, application users and other ...

Prolaio

Sr. Integrations Engineer (Backend)

Chicago, IL · On-site

Work directly with external partners and internal teams to negotiate data contracts, troubleshoot ... Deploy and manage containerized services on Google Cloud Platform (GCP) or similar, using Docker ...

Georgia IT Inc

Data Engineer

Chicago, IL · Remote

$118K - $141K/yr

DOE We are seeking an experienced Data Engineer with expertise in Alation for a 6-12 month contract ... Familiarity with cloud technologies such as Azure (preferable), Google Cloud, AWS , and Snowflake

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Showing results 1-20

All JobsContract Google Rf Engineer JobsContract Google Rf Engineer Jobs in Chicago, IL

Contract Google Rf Engineer information

See Chicago, IL salary details

$38.1K

$121.2K

$188.5K

How much do contract google rf engineer jobs pay per year?

As of Jun 9, 2026, the average yearly pay for contract google rf engineer in Chicago, IL is $121,228.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,400.00 and $143,200.00 per year, depending on experience, location, and employer.

What is the difference between Contract Google Rf Engineer vs Contract Wireless Network Engineer?

AspectContract Google Rf EngineerContract Wireless Network Engineer
CredentialsBachelor's in Electrical Engineering, RF or Wireless certificationsBachelor's in Telecommunications, RF certifications
Work EnvironmentTech company, R&D labs, product developmentTelecom providers, network deployment sites
Industry UsagePrimarily in tech and internet companiesTelecommunications and wireless service providers
Job FocusDesign and optimize RF systems for Google productsDeploy and maintain wireless networks

Contract Google Rf Engineers focus on designing and optimizing RF systems for Google's products, often working in R&D environments. In contrast, Contract Wireless Network Engineers typically deploy and maintain wireless networks for telecom providers. While both roles require RF expertise and certifications, their work settings and objectives differ significantly.

What are the most commonly searched types of Google Rf Engineer jobs in Chicago, IL? The most popular types of Google Rf Engineer jobs in Chicago, IL are:
Contract Google Rf Engineer jobs near you
GCP AI/ML Engineer

GCP AI/ML Engineer

Co-Sourcing Partners

Chicago, IL • On-site

Contractor

Posted 5 days ago

Job description

Job Title: GCP AI/ML Engineer
Duration: 6 months Contract to hire
Location: Chicago is the preferred location, but open to candidates from anywhere in the U.S.
Role Overview
We are seeking a talented and experienced GCP AI/ML Engineer to design, build, and operationalize scalable machine learning solutions on Google Cloud Platform (GCP). This role focuses on developing production-grade ML pipelines, automating workflows, and ensuring reliability and governance across enterprise AI platforms.
The ideal candidate will have strong expertise in Vertex AI, MLOps, and cloud-native ML architectures, with a passion for turning data science models into scalable, production-ready systems.
Key Responsibilities
ML Pipeline Development & Automation
  • Build, deploy, and manage production-grade machine learning pipelines using Vertex AI Pipelines and GCP-native services.
  • Design automated workflows for data ingestion, feature engineering, model training, evaluation, and inference.
  • Orchestrate ML workflows using Python, Vertex AI, BigQuery, and Cloud Storage.
  • Ensure pipelines are modular, reusable, and scalable across use cases.

Model Operationalization (MLOps)
  • Operationalize the end-to-end ML lifecycle, including:
  • Model training
  • Deployment
  • Monitoring
  • Retraining and lifecycle management
  • Deploy models using Vertex AI endpoints with support for online and batch predictions.
  • Implement robust CI/CD pipelines for ML artifacts and workflows.
  • Enable automated model retraining and versioning strategies.

Data Integration & Feature Engineering
  • Enable seamless data flows across data lakes, warehouses, and ML platforms.
  • Design and manage feature pipelines for training and inference datasets.
  • Integrate with BigQuery, Cloud Storage, and streaming sources to support real-time and batch ML use cases.
  • Ensure consistency between training and serving data pipelines.

Model Monitoring & Performance Optimization
  • Implement model monitoring solutions to track:
  • Prediction accuracy
  • Data drift and concept drift
  • Model performance degradation
  • Set up alerting mechanisms and dashboards for proactive issue detection.
  • Optimize model performance and infrastructure for scalability, latency, and cost efficiency.

AI Platform Engineering
  • Build and enhance enterprise AI/ML platforms with a focus on:
  • Automation
  • Observability
  • Reliability
  • Develop standardized frameworks for repeatable and governed ML deployments.
  • Establish best practices for MLOps, pipeline orchestration, and infrastructure management.

Collaboration & Cross-Functional Engagement
  • Collaborate closely with:
  • Data Scientists to productionize models
  • Data Engineers for data pipeline integration
  • Architects for scalable cloud designs
  • Translate business requirements into deployable ML solutions.
  • Provide technical leadership and mentoring on ML engineering practices.

Governance, Security & Best Practices
  • Implement model governance frameworks including auditability, lineage, and compliance.
  • Ensure secure handling of data and models using IAM roles and access policies.
  • Promote best practices in:
    • Code versioning (Git)
    • CI/CD
    • Testing and validation
  • Drive documentation and standardization across ML workflows.

Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field.
  • 4+ years of experience in machine learning engineering or MLOps.
  • Hands-on experience with Google Cloud Platform (GCP) services:
  • Vertex AI (Pipelines, Training, Endpoints)
    o BigQuery
    o Cloud Storage
  • Strong programming skills in Python.
  • Experience building and deploying end-to-end ML pipelines.
  • Strong understanding of ML lifecycle and MLOps principles.

Preferred Skills
  • Experience with TensorFlow, PyTorch, or Scikit-learn.
  • Familiarity with Kubeflow Pipelines or Apache Beam.
  • Experience with Docker and containerized deployments.
  • Knowledge of real-time ML inference and streaming architectures.
  • Hands-on experience with model monitoring tools and frameworks.
  • Understanding of feature stores and feature engineering pipelines.

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