DevOps Engineer (Machine Learning) – Remote

About the role

As a DevOps Engineer (ML) you will design, build, and operate the infrastructure that powers machine‑learning models in production. You act as the bridge between data‑science teams and operations, ensuring pipelines are scalable, reproducible, and highly available.

Key responsibilities

• Design and maintain CI/CD pipelines tailored for ML models and data workflows.
• Implement and scale model‑serving solutions using TorchServe, Triton, SageMaker, or KServe.
• Containerize ML applications and orchestrate them with Kubernetes and Docker.
• Develop monitoring and observability systems to detect performance drift and ensure system health.
• Collaborate with ML engineers to automate platform tooling for pipelines and deployments.

Required profile

• 4+ years of experience in DevOps, MLOps, or platform engineering.
• Proven expertise managing machine‑learning infrastructure with Kubernetes and Docker.
• Hands‑on experience with at least one ML serving framework (TorchServe, Triton, SageMaker, KServe).
• Strong Python programming skills and CI/CD implementation for ML.
• Solid understanding of model monitoring, observability, and drift detection.
• Advanced proficiency in English.

Required skills

• Kubernetes
• Docker
• Python
• CI/CD pipelines
• TorchServe
• Triton
• SageMaker
• KServe
• Model monitoring & observability

What we offer

• 100% remote work from anywhere.
• Competitive compensation in USD or local currency.
• Hardware and software setup for home office.
• Flexible hours and self‑managed schedule.
• Paid parental leave, vacations, and national holidays.
• Multicultural environment with mentorship, promotions, and skill‑development opportunities.