MLOps Cloud Engineer

Overview

We are seeking a Mid-Level ML Dev / Cloud Engineer to support the development, deployment, and optimization of machine learning services in a cloud-native environment. This role focuses on building scalable pipelines, integrating models into production, and ensuring reliable cloud infrastructure for ML applications. The ideal candidate has hands-on experience with ML workflow tools, cloud orchestration, and software development best practices.

Requirements

• 3–5 years of hands-on experience in machine learning engineering, MLOps, or cloud engineering .
• Strong foundations in Python , ML workflows, and API development.
• Experience deploying models into production using Docker / Kubernetes.
• Practical experience with at least one major cloud provider (AWS, GCP, or Azure).
• Familiarity with ML lifecycle tools (MLflow, Airflow, Kubeflow, or similar).
• Experience building or maintaining CI / CD pipelines.
• Understanding of distributed systems, container orchestration, and cloud-native architectures.
• Ability to collaborate with data scientists, engineers, and stakeholders.
• Excellent problem-solving skills and comfort working in a fast-paced environment.

Responsibilities

Tech Stack

Cloud Services (one or more) :

ML / MLOps Tools :

Languages & Frameworks :

DevOps & Infra :