Remote AI Engineer: MLOps & Production Pipelines

KATBOTZ; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Professional development

Flexible remote work

Dynamic work environment

Job summary

KATBOTZ LLC is seeking an AI Engineer / Machine Learning Engineer with strong MLOps experience to design, deploy, monitor, and maintain ML models in production environments. This position is remote and offers opportunities to work across cloud platforms and scalable ML pipelines.

You will build ML pipelines, automate model deployment with CI/CD, manage the model lifecycle, and ensure reliability, governance, and data quality while collaborating with data scientists and AI developers.

Qualifications

• 3–7 years in Machine Learning / AI / Data Engineering
• 2+ years in MLOps / Model Deployment / ML Pipelines
• Experience deploying models to production is mandatory

Responsibilities

• Build and maintain ML pipelines for training, testing, and deployment
• Deploy ML models into production environments
• Manage model lifecycle (training, deployment, monitoring, retraining)
• Automate workflows using CI/CD for ML models
• Monitor model performance, drift, and data quality
• Work with data scientists and AI developers to productionize models
• Manage model versioning, data versioning, and experiment tracking
• Deploy models on cloud platforms (AWS, Azure, GCP)
• Containerize applications using Docker and Kubernetes
• Implement monitoring and logging for ML systems
• Ensure scalability, security, and reliability of AI systems

Skills

Python

Machine Learning

MLOps

Docker

Kubernetes

CI/CD

MLflow

Kubeflow

Airflow

DVC

Weights & Biases

SageMaker

Azure ML

Vertex AI

Terraform

Data pipelines

APIs

AWS

Azure

GCP

SQL

NoSQL

Model monitoring

Tools

Docker

Kubernetes

GitHub Actions

Jenkins

GitLab CI

Job description

KATBOTZ LLC is seeking an AI Engineer / Machine Learning Engineer with strong MLOps experience to design, deploy, monitor, and maintain ML models in production environments. This position is remote and offers opportunities to work across cloud platforms and scalable ML pipelines.

You will build ML pipelines, automate model deployment with CI/CD, manage the model lifecycle, and ensure reliability, governance, and data quality while collaborating with data scientists and AI developers.