ML DevOps Architect: Cloud & Scalable Compute

Pathway; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Remote work

Job summary

Pathway is seeking a Machine Learning DevOps engineer to manage cloud-based ML pipelines, scalable infrastructure, and ML lifecycle automation. You will optimize GPUs and distributed compute, automate training/validation/deployment, and ensure reproducibility with strong containerization and CI/CD practices.

Ideal candidates have Linux, Terraform, Kubernetes, and Python experience, plus exposure to ML frameworks like TensorFlow or PyTorch. Remote-friendly with global team collaboration.

Qualifications

• Very good familiarity with Linux, shell scripts, and cluster configuration scripts.

Responsibilities

• Optimize infrastructure for ML training and inference (GPUs, distributed compute).
• Automate and maintain ML/LLM pipelines (data ingestion, training, validation, deployment).
• Manage model versioning, reproducibility, and traceability.
• Work with terabyte-large datasets.
• Implement ML-centric CI/CD practices.
• Monitor model performance and data drift in production.
• Collaborate with ML engineers, software engineers, and platform teams.

Skills

Linux

Shell scripting

Willingness to learn

Linux administration

Education

BSc in Computer Science or Information Technology

Tools

Slurm

Docker

Kubernetes

GitHub Actions

Jenkins

Gitlab CI

Terraform

CloudFormation

Kubeflow

Airflow

Metaflow

Grafana

CloudWatch

Prometheus

Loki

SageMaker Hyperpod

Vertex AI

TensorFlow

PyTorch

Python

Job description

Pathway is seeking a Machine Learning DevOps engineer to manage cloud-based ML pipelines, scalable infrastructure, and ML lifecycle automation. You will optimize GPUs and distributed compute, automate training/validation/deployment, and ensure reproducibility with strong containerization and CI/CD practices.

Ideal candidates have Linux, Terraform, Kubernetes, and Python experience, plus exposure to ML frameworks like TensorFlow or PyTorch. Remote-friendly with global team collaboration.