AWS GenAI Cloud DevOps Engineer

Broadridge Financial Solutions; City Of London; Hybrid; GBP 90,000 - 120,000; Full time

Benefits offered by this job

Hybrid work arrangement

Collaborative, inclusive culture

Education and training opportunities

Job summary

Broadridge Financial Solutions seeks an AWS Cloud DevOps Engineer to design, deploy, and support GenAI-focused ML capabilities across the organization. You will build scalable ML deployment pipelines, manage governance and security, and partner with data scientists to productionize models.

The role emphasizes end-to-end platform engineering in a hybrid environment, leveraging AWS, Terraform, Jenkins, Docker, and Kubernetes to deliver reliable, auditable ML systems.

Qualifications

• 5-8 years in cloud app development/administration with at least 4 years on AWS & Terraform, Jenkins, CloudFormation or similar IaC.
• Experience building end-to-end systems as a Platform Engineer/ML DevOps on AWS SageMaker or similar.
• Proficient with CI/CD, automation, logging and monitoring.

Responsibilities

• Design the ML deploy, monitoring pipelines and engineering infrastructure to support enterprise machine learning systems at scale.
• Turn offline models into production systems and operationalize them.
• Develop and deploy scalable tools and services for ML training and inference.
• Evaluate new technologies to improve performance, reliability and maintainability of ML systems.
• Apply software engineering rigor to ML, including CI/CD and automation.

Skills

Cloud automation

AWS

Terraform

Jenkins

CI/CD

Automation

Docker

Kubernetes

Shell scripting

Python

Go

Cloud networking

Tools

GitLab

Docker

Kubernetes

Job description

Broadridge Financial Solutions seeks an AWS Cloud DevOps Engineer to design, deploy, and support GenAI-focused ML capabilities across the organization. You will build scalable ML deployment pipelines, manage governance and security, and partner with data scientists to productionize models.

The role emphasizes end-to-end platform engineering in a hybrid environment, leveraging AWS, Terraform, Jenkins, Docker, and Kubernetes to deliver reliable, auditable ML systems.