Why work with us

How the job actually works

Remote-First Culture

Work from anywhere with flexible schedule 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

📌 Cloud Infrastructure Engineer (AI/ML Focus) (India)
🏢 Splashgain
📍 India