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Contract Remote Tensorflow Developer Jobs in Illinois

CoSourcing Partners

GCP AI/ML Engineer

Chicago, IL · On-site +1

Job Title: GCP AI/ML Engineer Duration: 6 months Contract to hire Location: Chicago is the ... Experience with TensorFlow, PyTorch, or Scikit-learn. * Familiarity with Kubeflow Pipelines or ...

Performacentric

Full Stack Developer

Chicago, IL · On-site +1

Remote (Preferred: Philippines, Latin America, or North America) Employment Type: Full-Time / ... Contract Company: Performacentric About Performacentric Performacentric helps small and mid-market ...

Tier4 Group

Sr. Application Developer

Deerfield, IL · Remote

$97K - $134K/yr

Remote with quarterly travel to Deerfield, IL (2 days on site per week preferred for local ... Permanent/Direct Hire OR Contract-to-Hire Overview We are seeking a Senior Application Developer to ...

Talent Management Plus, Inc.

ServiceNow Developer

Chicago, IL · Remote

$55.75 - $76.50/hr

REMOTE Duration: 6 months+ Summary: ServiceNow Developer with specialized experience in Software ... Configure SAM features to track software usage, license compliance, entitlements, contracts, and ...

BetterHelp

Psychiatrist - (Remote)

Chicago, IL · Remote

$128 - $175/hr

Flexible commitment: Part-time, 1099 contract position What You'll Do * Conduct video consultations ... Share product and workflow feedback with UpLift's engineering and operations teams to help improve ...

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GCP AI/ML Engineer

GCP AI/ML Engineer

CoSourcing Partners

Chicago, IL • On-site, Remote

Other

Posted 21 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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