Overview
We are looking for passionate engineers to design, build, and secure robust cloud infrastructure that supports scalable and high-availability platforms across our division's digital projects. You will play a key role in architecting cloud solutions, implementing automation, and ensuring system reliability through modern DevOps and security practices while supporting AI pipelines and Machine Learning Operations (MLOps) in production environments.

This role requires hands-on exposure to AI workload deployment, model pipelines, monitoring, and lifecycle management, incollaboration with software, data, and AI teams.

Responsibilities

• Design and implement cloud architecture solutions using AWS services.
• Manage and maintain core AWS services (EC2, S3, RDS, Lambda, VPC, IAM).
• Develop and deploy Infrastructure as Code (IaC) using Terraform or CloudFormation.
• Monitor cloud systems to ensure performance, scalability, and cost efficiency.
• Implement cloud security best practices and maintain compliance with organisational standards.
• Designand support AI and data pipelines, including infrastructure for model training,inference, and batch processing
• Support Machine Learning Operations (MLOps) activities such as model deployment, versioning, monitoring, and rollback.
• Design and manage CI/CD pipelines with integrated security and testing controls.
• Collaborate closely with development teams to ensure smooth, secure deployment and operations.
• Provide technical guidance and share best practices on cloud adoption and optimisation.

Tech Stack

• Cloud Platform: Amazon Web Services (AWS)
• Infrastructure as Code: Terraform, CloudFormation
• CI/CD: Jenkins, GitLab CI
• Containerisation: Docker, Kubernetes
• Tools: JIRA, Confluence, Git, GitLab

AI / MLOps (Exposure & Competency)

• Infrastructure for ML training and inference
• AI pipeline orchestration and deployment
• Model monitoring, logging, and performance tracking

Key Attributes

• Strong understanding of cloud architecture principles and distributed systems.
• Hands-on experience with the AWS ecosystem and its core services.
• Understanding of AI pipelines and MLOps concepts, including model deployment and monitoring will be regarded.
• Knowledge of system monitoring, disaster recovery, and cloud security practices.
• Strong problem-solving and analytical skills with attention to detail.
• Self-driven and curious about emerging cloud and DevOps technologies.
• Collaborative team player with clear communication and documentation skills.

Additional Information

• AWS certifications (e.g., Solutions Architect, DevOps Engineer) are advantageous.
• Candidates with relevant experience may be considered for senior positions.
• Fresh graduates with strong technical fundamentals and a passion for cloud technologies are welcome to apply.