ML DevOps Architect: Cloud & Large-Scale Compute (Remote)

Ignite Next GmbH; Palo Alto, Northern (CA, KY); Hybrid; USD 140,000 - 200,000; Full time

Benefits offered by this job

Remote work

Office visits in Palo Alto, Paris, orW

Flexible relocation options

Job summary

Pathway is seeking a Machine Learning DevOps engineer to optimize ML infrastructure in the cloud, automate ML/LLM pipelines, and support scalable model deployment. The role focuses on reliable, scalable, and auditable ML lifecycle operations across distributed systems.

You will manage GPUs and distributed compute, monitor performance, and collaborate with ML engineers, software engineers, and platform teams to scale production workloads.

Qualifications

• BSc in Computer Science or Information Technology is required.
• Hands-on experience in Linux, shell scripting, and cluster management.
• Strong knowledge of Python and ML pipelines.
• Proficiency with CI/CD tools and workflows.
• Experience with ML pipeline orchestration tools (MLflow, Kubeflow, Airflow, Metaflow).
• Familiarity with cloud services across AWS/GCP/Azure and monitoring stacks.

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-scale datasets.
• Implement ML-centric CI/CD practices.
• Monitor model performance and data drift in production.

Skills

Linux

Shell scripting

Python

CI/CD

ML pipelines

Cloud platforms

Team collaboration

Education

BSc in Computer Science or Information Technology

Tools

Slurm

Docker

Kubernetes

Terraform

MLflow

Kubeflow

Airflow

Metaflow

Grafana

CloudWatch

Prometheus

Loki

Job description

Pathway is seeking a Machine Learning DevOps engineer to optimize ML infrastructure in the cloud, automate ML/LLM pipelines, and support scalable model deployment. The role focuses on reliable, scalable, and auditable ML lifecycle operations across distributed systems.

You will manage GPUs and distributed compute, monitor performance, and collaborate with ML engineers, software engineers, and platform teams to scale production workloads.