Remote Senior MLOps Engineer: Build Production AI Pipelines

clariticloudinc; United States; Remote; USD 105,000 - 140,000; Full time

Job summary

clariticloudinc is seeking the first MLOps engineer to build production-ready ML infrastructure, spanning pipelines, cloud compute, model serving, and automation. You will work closely with AI researchers and engineers to create reproducible, scalable, observable, and cost-conscious systems.

The role covers end-to-end pipelines, model training and serving, CI/CD, monitoring for drift and data quality, and cost-optimization of training workloads in a remote setup with occasional in-person travel.

Qualifications

• Six or more years in software or machine-learning engineering, including at least three years in MLOps or ML infrastructure.
• Strong programming experience with Python, C/C++, or Bash.
• Demonstrated experience deploying and operating production ML models.

Responsibilities

• Build and maintain end-to-end pipelines for model training, evaluation, deployment, and agent-based AI workflows.
• Develop and optimize infrastructure for training and serving models across cloud providers and compute environments.
• Create tools for data pipelines, labeling, experiment and performance tracking, and automated retraining.
• Help research and engineering teams productionize prototypes with reproducible, scalable workflows.
• Establish CI/CD practices, automated testing, and deployment processes for ML systems.
• Monitor production model health, performance, drift, and data quality; tune performance and training costs.

Skills

Python

C/C++

Bash

Docker

Kubernetes

MLflow

Weights & Biases

Kubeflow

CI/CD

Experiment tracking

Tools

Docker

Kubernetes

MLflow

Weights & Biases

Kubeflow

Job description

clariticloudinc is seeking the first MLOps engineer to build production-ready ML infrastructure, spanning pipelines, cloud compute, model serving, and automation. You will work closely with AI researchers and engineers to create reproducible, scalable, observable, and cost-conscious systems.

The role covers end-to-end pipelines, model training and serving, CI/CD, monitoring for drift and data quality, and cost-optimization of training workloads in a remote setup with occasional in-person travel.