Senior Cloud & ML Infrastructure Engineer

Key Responsibilities :

• Architect, deploy, and manage scalable ML infrastructure on cloud platforms (AWS, GCP, or Azure).
• Design and maintain end-to-end ML pipelines for training, testing, and deploying models.
• Work with Kubernetes, Docker, and CI / CD to automate ML workflows and deployments.
• Collaborate with data scientists to optimize model training and inference performance.
• Implement monitoring, logging, and alerting systems for ML applications in production.
• Ensure data security, compliance, and cost optimization in cloud environments.
• Integrate distributed computing frameworks (Spark, Ray, Dask, etc.) for large-scale data processing.
• Research and adopt best practices for MLOps and cutting-edge ML infrastructure technologies.

Requirements :

(ref : hirist.tech)