Remote AI/ML Infrastructure Lead — Scale Cloud Pipelines

Placements24; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Home office allowance

Generous PTO

Conference attendance

Job summary

WhatJobs Direct seeks an experienced Remote AI/ML Infrastructure Lead to design, build, and scale the company’s AI/ML infrastructure across cloud platforms. You will lead a distributed team, establish best practices for MLOps, and drive reliability, security, and performance of data pipelines and model deployments.

The role requires deep cloud and distributed systems expertise, leadership capabilities, and a passion for enabling AI at scale in a fully remote setting.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• 8+ years in infrastructure, DevOps, or cloud architecture with AI/ML focus.
• Experience leading infrastructure teams and complex projects.
• Expertise in AWS, Azure, or GCP and their AI/ML services.

Responsibilities

• Lead design and implementation of scalable AI/ML infrastructure on cloud platforms.
• Define and enforce MLOps best practices (CI/CD, model versioning, deployment).
• Manage cloud resources for cost and performance.
• Oversee data pipelines and storage for ML workloads.
• Mentor infrastructure engineers and DevOps teams.
• Collaborate with data scientists to meet infra needs.
• Ensure reliability, security, and availability of the platform.
• Evaluate new technologies to enhance infra.
• Develop documentation for infrastructure.
• Lead incident response for infra issues.

Skills

AI/ML infrastructure

Leadership

DevOps

MLOps

Terraform

Docker

Kubernetes

Python

Cloud cost optimization

Education

Bachelor's or Master's in CS/Engineering

Tools

MLflow

Kubeflow

Airflow

Terraform

CloudFormation

Job description

WhatJobs Direct seeks an experienced Remote AI/ML Infrastructure Lead to design, build, and scale the company’s AI/ML infrastructure across cloud platforms. You will lead a distributed team, establish best practices for MLOps, and drive reliability, security, and performance of data pipelines and model deployments.

The role requires deep cloud and distributed systems expertise, leadership capabilities, and a passion for enabling AI at scale in a fully remote setting.