Careers

Network and Cloud AI Engineer

• Location: Basingstoke
• Job Type: Full-time
• Department: AI & Infrastructure / Cloud Engineering
• Salary: £42,000 per year
• Start Date: 11.06.2025
• Closing Date: 11.07.2025

About the Role

We are seeking a Network and Cloud AI Engineer to architect, deploy, and optimize our AI infrastructure across cloud and hybrid environments. This role combines networking fundamentals with hands-on experience in AI/ML deployment, cloud infrastructure, and data pipelines. You will work with data scientists, cloud architects, and DevOps to deliver scalable, secure, and high-performance AI solutions.

Responsibilities

• Design, deploy, and manage cloud-native AI platforms (Databricks, Azure ML, SageMaker, GCP Vertex AI)
• Optimize network connectivity and bandwidth for distributed AI workloads
• Provision and maintain GPU/TPU instances
• Manage Kubernetes, Docker, and model deployments
• Configure VPCs, load balancers, firewalls, and VPNs
• Ensure secure communication between edge devices, data sources, and cloud endpoints
• Implement IAM policies, encryption, and access controls
• Support end-to-end AI workflows (preprocessing, training, deployment, monitoring)
• Integrate and automate pipelines (MLflow, Kubeflow, CI/CD)
• Monitor model performance and infrastructure costs
• Optimize models for edge, hybrid, or cloud environments
• Collaborate on deployment and monitoring with data scientists and DevOps
• Document architecture, processes, and infrastructure
• Participate in code reviews, architecture decisions, incident response

Qualifications

• Bachelor’s or Master’s in Computer Science, Engineering, or related
• 3–7 years in cloud infrastructure, networking, or AI deployment
• Hands-on AWS / Azure / GCP networking and AI tools
• Containerization (Docker), orchestration (Kubernetes)
• Strong with network protocols, VPCs, subnets, routing, firewalls
• Familiarity with ML lifecycle tools (MLflow, SageMaker, Azure ML)
• Programming proficiency: Python and Bash/PowerShell
• Certifications (Mandatory):
  • AWS Certified Machine Learning – Specialty
  • Microsoft Certified: Azure AI Engineer Associate
  • Google Professional Machine Learning Engineer

• Experience with data streaming technologies: Kafka, Pub/Sub, Event Hub
• Model optimization for edge (TensorRT, ONNX)
• Experience in regulated industries (finance, healthcare, telecom)

Tools & Technologies

• Cloud: AWS, Azure, GCP
• AI Platforms: Databricks, MLflow, Kubeflow, SageMaker, Azure ML
• Networking: VPC, DNS, Load Balancers, VPN, Private Link
• DevOps: Terraform, Ansible, GitHub Actions, Jenkins
• Languages: Python, SQL, Bash

Benefits

• Competitive salary + bonus
• Flexible remote work options
• Cloud and AI certification sponsorship
• Health, dental, and vision insurance
• Learning & development budget