Senior DevOps Engineer, Cloud & ML Infra

Adobe; San Jose (CA); On-site; USD 178,000 - 258,000; Full time

Job summary

Adobe seeks a Senior DevOps Engineer to build and manage a large, globally distributed infrastructure using Reliability Engineering and DevOps methods.

You will work at the intersection of engineering and operations for Search & Sensei products, deploying on AWS/Azure, and using Elastic Search, Kafka, Docker, Kubernetes, Hadoop, HBase, Spark, MongoDB, Terraform, and Jenkins to deliver scalable services.

Qualifications

• B.S. in Engineering or equivalent experience.
• 3-7 years of meaningful industry experience.
• Experience in building, deploying and managing infrastructures in public clouds (AWS, Azure).
• Excellent programming (Perl/Python/Ruby/Shell/Go/Java) and automation skills.
• Exposure to large-scale NO-SQL technologies such as ElasticSearch, Kafka, HBase, MongoDB, Apache Spark, etc.
• Exposure to Linux/Unix System Administration.
• Experience with Git, Perforce, and Jenkins; exposure to cloud/virtualization technologies.
• Knowledge of Cloud well-architected frameworks and cloud build principals.

Responsibilities

• Develop and manage large, globally distributed infrastructure.
• Set up cloud-based technologies to enable the Search & Sensei platform.
• Maintain and optimize the search engine and related data pipelines.
• Implement high availability and scalable infrastructure solutions.
• Reduce costs while improving performance and reliability.

Skills

Python

Java

Go

Shell

Perl

Ruby

Linux

AWS

Azure

ElasticSearch

Kafka

Docker

Kubernetes

Hadoop

MongoDB

Spark

Terraform

Jenkins

Education

B.S. in Engineering or equivalent

Tools

Docker

Kubernetes

Terraform

Jenkins

ElasticSearch

Kafka

MongoDB

Apache Spark

Job description

Adobe seeks a Senior DevOps Engineer to build and manage a large, globally distributed infrastructure using Reliability Engineering and DevOps methods.

You will work at the intersection of engineering and operations for Search & Sensei products, deploying on AWS/Azure, and using Elastic Search, Kafka, Docker, Kubernetes, Hadoop, HBase, Spark, MongoDB, Terraform, and Jenkins to deliver scalable services.