GenAI Platform DevOps Engineer (AWS)

eFinancialCareers; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Broadridge is seeking an AWS Cloud DevOps Engineer to join the AI Enablement Team. You will architect, deploy and support solutions that enhance AI capabilities, focusing on GenAI projects and enterprise ML pipelines.

In this role you will design scalable deployment, monitoring, and tooling, while collaborating with data scientists and engineers to deliver production-grade ML systems and ensure security and auditability.

Qualifications

• 5–8 years in cloud app development with at least 4 years on AWS & IaC tools.
• Hands-on experience with end-to-end platform engineering and ML DevOps on AWS.
• Good knowledge of CI/CD and automation tools.

Responsibilities

• Design the ML deploy, monitoring pipelines and engineering infrastructure to support enterprise machine learning systems at scale.
• Turn offline models data scientists build into a real ML production system.
• Develop and deploy scalable tools and services to handle ML training and inference.
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of ML systems.
• Apply software engineering rigor and best practices to ML, including CI/CD, automation, etc.
• Support model development with emphasis on auditability, versioning, and data security.
• Facilitate the development and deployment of proof-of-concept ML systems.
• Communicate with clients to build requirements and track progress.

Skills

Cloud app development

AWS expertise

CI/CD automation

Team communication

Problem solving

Tools

Terraform

Jenkins

CloudFormation

Docker

Kubernetes

GitLab

Job description

Broadridge is seeking an AWS Cloud DevOps Engineer to join the AI Enablement Team. You will architect, deploy and support solutions that enhance AI capabilities, focusing on GenAI projects and enterprise ML pipelines.

In this role you will design scalable deployment, monitoring, and tooling, while collaborating with data scientists and engineers to deliver production-grade ML systems and ensure security and auditability.