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Temporary Computer Vision Deep Learning Engineer Jobs in Illinois

Ontrac Solutions

Machine Learning Engineer

Chicago, IL · On-site

$70 - $90/hr

Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or equivalent practical experience. Required Qualifications * Solid hands-on experience with the GCP ecosystem ...

Capital One

Senior Machine Learning Engineer

Chicago, IL · On-site +1

$107.60K - $147.80K/yr

Senior Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part ... Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar ...

Ontrac Solutions

Machine Learning Engineer

Chicago, IL · On-site

$70 - $90/hr

Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or equivalent practical experience. Required Qualifications * Solid hands-on experience with the GCP ecosystem ...

Launch Consulting

Senior Data Scientist

Chicago, IL · On-site

$140K - $180K/yr

... computer vision, and other cutting-edge techniques. Key Responsibilities: * Lead advanced Data ... deep learning, and NLP. * Conduct rigorous exploratory data analysis and feature engineering to ...

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Temporary Computer Vision Deep Learning Engineer information

What is the difference between Temporary Computer Vision Deep Learning Engineer vs Computer Vision Engineer?

AspectTemporary Computer Vision Deep Learning EngineerComputer Vision Engineer
CredentialsBachelor's or Master's in CS, AI, or related; experience with deep learning frameworksBachelor's or Master's in CS, AI, or related; experience with computer vision tools
Work EnvironmentProject-based, short-term contracts, often in tech or research firmsFull-time, ongoing roles in tech companies, startups, or research labs
Industry UsageCommon in consulting, research projects, or temporary assignmentsStandard role in product development, AI solutions, and software engineering

The main difference is that a Temporary Computer Vision Deep Learning Engineer works on short-term projects focusing on deep learning techniques for computer vision, while a Computer Vision Engineer typically holds a permanent position involved in ongoing development of computer vision applications. The temporary role emphasizes flexibility and project-specific skills, whereas the full-time role involves continuous integration into a company's long-term projects.

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Temporary Computer Vision Deep Learning Engineer jobs near you

Machine Learning Engineer

Ontrac Solutions LLC

Chicago, IL • On-site

$70 - $90/hr

Contractor

Posted 2 days ago

Job description

Ontrac Solutions is seeking Machine Learning Engineers to support an urgent staff augmentation engagement for one of our clients.
This role is ideal for junior-to-mid-level engineers with strong Google Cloud Platform experience and a focus on building, maintaining, and supporting production-grade machine learning systems.
The selected engineers will work under the direct guidance of a Staff ML Architect and will focus heavily on daily MLOps execution, pipeline maintenance, model reliability, and production support for a high-traffic digital platform.
Required Credentials
  • 2+ years of experience in machine learning engineering, data engineering, software engineering, or a related technical role.
  • Hands-on experience supporting production or near-production ML systems.
  • Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or equivalent practical experience.
Required Qualifications
  • Solid hands-on experience with the GCP ecosystem, particularly Vertex AI components such as Workbench, Pipelines, and Model Registry.
  • Proficiency with modern ML frameworks, including PyTorch or similar technologies.
  • Experience with containerization tools, especially Docker, for automated builds and deployments.
  • Practical experience managing data processing workflows using Apache Spark and Airflow.
  • Understanding of MLOps best practices, including model deployment, monitoring, training workflows, inference support, and pipeline reliability.
  • Familiarity with real-time model serving and infrastructure tools such as Triton Inference Server and Terraform is highly preferred.
  • Strong problem-solving skills with the ability to troubleshoot, maintain, and optimize ML pipelines in a production environment.
  • Collaborative mindset with the ability to execute technical tasks reliably under the guidance of a senior architect.
Key Responsibilities
  • Support the design, deployment, monitoring, and maintenance of machine learning models in a high-traffic production environment.
  • Maintain, troubleshoot, and optimize end-to-end ML pipelines from raw data ingestion through offline and online model evaluation.
  • Execute daily MLOps tasks, including model training, inference support, pipeline monitoring, and deployment maintenance.
  • Work with tools such as GCP, Vertex AI, Spark, Airflow, Docker, PyTorch, and related MLOps technologies.
  • Build and manage automated containerized deployments to support continuous model operations.
  • Partner closely with the Staff ML Architect and other ML Engineers to ensure models are reliable, scalable, and production-ready.
  • Help identify and resolve performance, reliability, and scalability issues across ML workflows and infrastructure.
Preferred Qualifications
  • Prior experience supporting high-traffic digital platforms or consumer-facing products.
  • Experience with Triton Inference Server, Terraform, or similar infrastructure and real-time serving tools.
  • Experience working in staff augmentation, consulting, or fast-moving client-facing environments.
  • Strong interest in building reliable, production-grade ML systems rather than only experimental or research-focused models.
About Ontrac Solutions
Ontrac Solutions is a strategic consulting and technology solutions firm helping companies Innovate. Create. Elevate. through digital product consulting, cloud solutions, AI-based data solutions, and staff augmentation.
We partner with clients to bring the right technical expertise, execution support, and strategic guidance to complex business and technology initiatives.

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