MLOps Engineer II — Production AI Infra & CI/CD

Openkyber, LLC; Atlanta (GA); On-site; USD 110,000 - 150,000; Full time

Job summary

Openkyber, LLC is seeking an ML OPS AI Engineer II to support production-grade ML solutions from development through live deployment in a hands-on role.

The ideal candidate brings 3–5 years in MLOps or AI infrastructure, strong Docker/Kubernetes experience, and proficiency in Python with IaC and CI/CD knowledge. This is a long-term contract with on-site work in Coppell, TX and cloud-based scalability considerations.

Qualifications

• 3 to 5 years in MLOps, ML platform engineering, or AI infrastructure roles.
• Experience deploying and maintaining ML models in production.
• Hands-on with Azure and compute resources for training/inference.
• Strong containerization/orchestration: Docker and Kubernetes.
• Experience building CI/CD pipelines for ML or software delivery.
• Proficiency in Python and automation / IaC practices.
• Bachelors or Masters in CS/Engineering or related field.
• Familiarity with AI/ML concepts and agile environments.

Responsibilities

• Operationalize machine learning models from development to production.
• Develop ML infrastructure, automated pipelines, and deployment frameworks.
• Create and manage containerized workloads with Docker; coordinate services via Kubernetes.
• Establish CI/CD for model training, packaging, testing, and release.
• Implement experiment tracking, feature lineage, and model version control.
• Build monitoring for system health, model behavior, and data drift.
• Provision and optimize cloud resources for training and inference.
• Improve scalability and cost efficiency across AI services.
• Collaborate with data scientists and ML engineers to streamline deployment.

Skills

MLOps

Automation

Agile environment

Model deployment

Observability

Education

Bachelor's or Master's in CS/Engineering

Tools

Docker

Kubernetes

Azure

Python

CI/CD

Job description

Openkyber, LLC is seeking an ML OPS AI Engineer II to support production-grade ML solutions from development through live deployment in a hands-on role.

The ideal candidate brings 3–5 years in MLOps or AI infrastructure, strong Docker/Kubernetes experience, and proficiency in Python with IaC and CI/CD knowledge. This is a long-term contract with on-site work in Coppell, TX and cloud-based scalability considerations.