Remote AI Engineer: Build Scalable ML Pipelines

YO IT Consulting; Chicago (IL); On-site; USD 82,656 - 165,312; Part time

Job summary

YO IT Consulting is seeking an AI Engineer for a remote contract role. You will apply your expertise to train next-generation AI systems and shape how models learn, reason, and perform through high-quality, real-world inputs.

Collaborate with data scientists and engineers to translate business problems into ML solutions, design scalable pipelines on AWS, and deploy containerized workloads with Kubernetes. Strong communication and documentation will be essential.

Qualifications

• Proven expertise in ML algorithms, model development and deployment.
• Strong programming skills in Python or Java for large-scale projects.
• Hands-on experience with CI/CD workflows and automation tools.
• Hands-on experience with AWS for ML and data pipelines.
• Advanced knowledge of Kubernetes for orchestrating ML workloads.
• Excellent written and verbal communication for documentation.

Responsibilities

• Design, build, and optimize ML models for production environments.
• Implement and automate end-to-end ML pipelines with CI/CD.
• Leverage AWS services for scalable AI infra and deployment.
• Orchestrate containerized workloads with Kubernetes for scalability.
• Collaborate with data scientists, engineers, researchers to translate problems.
• Evaluate and preprocess real-world data for ML applications.
• Document findings and technical decisions clearly.

Skills

Machine learning concepts

Programming in Python/Java

CI/CD workflows

Data problem solving

Strong communication

Tools

AWS

Kubernetes

TensorFlow or PyTorch

Job description

YO IT Consulting is seeking an AI Engineer for a remote contract role. You will apply your expertise to train next-generation AI systems and shape how models learn, reason, and perform through high-quality, real-world inputs.

Collaborate with data scientists and engineers to translate business problems into ML solutions, design scalable pipelines on AWS, and deploy containerized workloads with Kubernetes. Strong communication and documentation will be essential.