Cloud Infrastructure Engineer (AI/ML Focus)

Why work with us

How the job actually works

Remote-First Culture

Work from anywhere with flexible hours and global collaboration.

Learning & Development

Continuous learning opportunities and skill development programs.

Work-Life Balance

Flexible schedules and time off to maintain healthy work-life balance.

Selection process

What happens after you write to us

• 1

  AI Telephonic Interview

  A short screening call with our AI interviewer, on any phone, at a time that suits you.

• 2

  AI Video Interview

  Role-specific questions answered on video, reviewed by our team.

• 3

  Interview / Technical Test

  An interview with team members, or a practical test of your skills.

• 4

  Offer

  Final decision and offer discussion.

Cloud Infrastructure Engineer (AI/ML Focus)

Engineering In-office / Hybrid 4-7 years Full-Time

Build and maintain scalable cloud infrastructure for AI-powered assessment and proctoring platforms. Design systems that support real-time video streaming, ML model deployment, and high-traffic applications.

What you’ll do

• Design and deploy cloud infrastructure for AI/ML workloads on AWS, GCP, or Azure
• Deploy and manage N8N infrastructure for Agentic AI workflows and autonomous agent orchestration
• Implement auto-scaling infrastructure for video streaming and real-time proctoring services
• Set up ML model deployment pipelines using AWS SageMaker, Google Vertex AI, or Azure ML
• Configure infrastructure for AI agent systems, including N8N, LangChain services, and vector databases
• Configure CDN and edge computing solutions for low-latency video delivery (CloudFront, Cloudflare)
• Implement monitoring and observability using Prometheus, Grafana, ELK Stack, or Datadog
• Design secure network architecture with VPC, security groups, and NAT gateways
• Optimize database performance for high-throughput SQL (MS SQL Server) and NoSQL workloads
• Implement Infrastructure as Code using Terraform, CloudFormation, or Pulumi
• Set up CI/CD pipelines for ML models, AI agents, and microservices deployment

Required skills & technologies

• Cloud Platforms: AWS (EC2, Lambda, SageMaker, ECS/EKS), GCP (GKE, Vertex AI), Azure
• Agentic AI Infrastructure: Experience deploying and managing autonomous AI agent systems and multi-agent frameworks
• N8N Deployment: Hands-on experience deploying N8N workflows, managing N8N infrastructure, and scaling agent orchestration systems
• Container Orchestration: Kubernetes, Docker Swarm, AWS ECS/Fargate
• Infrastructure as Code: Terraform, AWS CloudFormation, Ansible, Pulumi
• CI/CD: Jenkins, GitLab CI, GitHub Actions, ArgoCD
• Monitoring: Prometheus, Grafana, CloudWatch, Azure Monitor, Datadog
• Databases: MS SQL Server (required), PostgreSQL, MySQL, MongoDB, Redis, DynamoDB, Vector databases (Pinecone, Weaviate)
• Video Streaming: AWS IVS, Agora, WebRTC infrastructure, TURN/STUN servers
• Security: IAM, Secret Management (Vault, AWS Secrets Manager), SSL/TLS
• Networking: VPC, Load Balancers, NAT Gateway, VPN, CDN configuration
• ML Infrastructure: MLflow, Kubeflow, SageMaker Pipelines
• Scripting: Python, Bash, PowerShell
• Frontend deployment: Experience deploying Angular applications and SPAs