Cloud Engineer

Cloud & DevOps Engineer

Location: New Delhi / Hybrid

Company: a21.ai

Experience: 3–6 years

Employment: Full-time

About a21.ai

a21.ai is an enterprise AI engineering company helping organizations build and deploy production-grade Generative AI and Agentic AI systems. We work across cloud infrastructure, AI platforms, data engineering and enterprise application integration, with AWS as a major technology platform.

We are looking for a Cloud & DevOps Engineer who is comfortable using AI as part of their engineering workflow — not just for answering questions, but for generating infrastructure code, automating repetitive operations, troubleshooting systems, writing scripts and accelerating cloud migrations.

What You'll Own

You will own the cloud infrastructure and DevOps layer of AI applications and cloud migration projects, including:

• Designing and deploying production environments on AWS
• Migrating workloads between AWS, Azure, GCP and on-premise environments
• Designing VPCs, subnets, routing, security groups, IAM and multi-account environments
• Building cloud landing zones and target architectures
• Provisioning infrastructure using Terraform, AWS CloudFormation and/or AWS CDK
• Building and maintaining CI/CD pipelines
• Deploying containerized workloads using Docker, ECS and Kubernetes/EKS
• Working with services such as EC2, Lambda, S3, RDS, DynamoDB, API Gateway, CloudFront, Route 53, IAM and Secrets Manager
• Deploying infrastructure supporting Amazon Bedrock and Generative AI workloads
• Implementing monitoring, logging and alerting using CloudWatch and other observability platforms
• Cloud cost optimization and resource-right-sizing
• Security hardening, IAM policies, secrets management and infrastructure governance
• Planning production cutovers, rollback strategies and migration validation
• Troubleshooting cloud, networking, deployment and production infrastructure issues
  

AI-Native Engineering

This is an important part of the role. We expect you to use AI coding agents and copilots as everyday engineering tools. You should be comfortable using tools such as Claude Code, Codex, Kiro, Amazon Q Developer, GitHub Copilot or equivalent tools to:

• Generate Terraform and CloudFormation templates
• Convert architecture requirements into Infrastructure-as-Code
• Generate and improve CI/CD pipelines
• Write Bash and Python automation scripts
• Analyze logs and troubleshoot infrastructure issues
• Generate IAM policies and validate permissions
• Refactor and review infrastructure code
• Generate documentation and architecture descriptions
• Accelerate cloud migration assessments
• Convert manual cloud configurations into reproducible infrastructure
• Investigate unfamiliar cloud services quickly

We do not expect AI-generated infrastructure to be blindly deployed. You must understand the infrastructure well enough to review, validate, secure, test and take ownership of everything generated by AI. The objective is simple: an engineer using AI effectively should be able to execute significantly faster than an engineer working entirely manually.

Must-Have Skills
AWS

Strong hands-on experience with:

• VPC, Subnets, NAT Gateway, Transit Gateway and routing
• IAM roles, policies and cross-account access
• EC2
• S3
• RDS
• Lambda
• API Gateway
• CloudFront
• Route 53
• CloudWatch
• Secrets Manager / Parameter Store
• Load Balancers and Auto Scaling
  

Infrastructure-as-Code

Strong hands-on experience with at least one of:

• Terraform
• AWS CloudFormation
• AWS CDK

Terraform experience is strongly preferred.

You should understand:

• Modules and reusable infrastructure
• Remote state
• Environment separation
• Variables and secrets
• Drift
• Importing existing infrastructure
• Infrastructure testing and validation
• IaC deployment through CI/CD
  

DevOps

Experience with:

• Git and GitHub/GitLab
• GitHub Actions, GitLab CI, Jenkins or equivalent
• Docker
• Linux
• Bash
• Python scripting
• CI/CD design
• Artifact and container registries
• Automated deployments
• Environment and secrets management
  

Containers

Working knowledge of:

• Docker
• Amazon ECS/Fargate
• Kubernetes
• Amazon EKS

You don't need to be a Kubernetes specialist, but you should be comfortable deploying and troubleshooting containerized applications.

Cloud Migration

You should understand how to assess and migrate workloads between cloud environments.

This includes:

• Application and infrastructure discovery
• Dependency mapping
• Source-to-target service mapping
• Network architecture
• IAM and security migration
• VM and container migration
• Database migration
• Storage migration
• DNS and traffic cutover
• Infrastructure recreation through IaC
• Migration waves
• Testing and validation
• Rollback planning
• Post-migration optimization

Experience migrating Azure/GCP workloads to AWS is particularly valuable.

Good to Have

• Azure or GCP experience
• AWS Organizations / Control Tower
• Multi-account AWS architecture
• AWS Well-Architected Framework
• AWS Migration Hub / Application Migration Service
• AWS Transform
• AWS DMS
• EKS production operations
• Helm
• ArgoCD
• Prometheus / Grafana
• OpenTelemetry
• FinOps / AWS cost optimization
• Security and compliance environments
• Experience supporting Generative AI or ML infrastructure
• Amazon Bedrock
• OpenSearch
• Vector databases
• GPU infrastructure
  

What We're Looking For

We are not looking for someone whose primary skill is operating cloud consoles manually.

We want someone who thinks:

How can I automate this

and increasingly:

How can I use AI to automate this safely

You should be able to take a requirement such as:

Create a production-ready AWS environment with private ECS services, ALB, RDS, S3, CloudFront, monitoring and least-privilege IAM

and independently convert it into:

Architecture → Terraform/CloudFormation → CI/CD → Deployment → Monitoring → Documentation

with AI helping you execute faster at each step.

Success in This Role

Within your first few months, you should be able to independently:

• Provision complete AWS environments using IaC
• Build deployment pipelines for a21.ai applications
• Support production Generative AI workloads
• Diagnose cloud and deployment failures
• Execute significant parts of a cloud migration
• Create reusable Terraform modules and deployment patterns
• Use AI agents to materially reduce infrastructure engineering time
• Review AI-generated code for security, reliability and cost issues
• Work directly with AI, application and data engineers on customer projects
  

Certifications

AWS certifications are valuable but hands-on ability matters more than certifications.

Relevant certifications include:

• AWS Certified Solutions Architect – Associate / Professional
• AWS Certified DevOps Engineer – Professional
• AWS Certified CloudOps Engineer
• Terraform Associate