Key Responsibilities
- Design, implement, and maintain AWS infrastructure including networking, compute, storage, and
IAM to support production and non-production environments.
- Build and manage infrastructure-as-code (IaC) using tools such as Terraform or AWS
CloudFormation for repeatable and automated provisioning.
- Architect and optimize environments to support agentic AI workloads, including model hosting, data
pipelines, and integration with AI/ML services.
- Implement monitoring, logging, and alerting solutions (e.g., CloudWatch, CloudTrail, OpenSearch,
Prometheus/Grafana) to ensure reliability and observability.
- Ensure security best practices across the AWS stack including VPC design, security groups,
encryption, secrets management, and compliance controls.
- Collaborate with application, data, and AI teams to design CI/CD pipelines and deployment strategies
for cloud-native and containerized workloads (with strong focus on Amazon EKS/ECS).
- Perform performance tuning, capacity planning, and cost optimization using AWS-native tools and
governance practices.
- Troubleshoot complex infrastructure issues, perform root-cause analysis, and contribute to
documentation and knowledge sharing.
Required Skills
- Strong hands-on experience with AWS core services (EC2, S3, VPC, IAM, RDS/Aurora, Lambda,
Load Balancers, CloudWatch).
- Proven experience in infrastructure design, implementation, and operations for large-scale or
business-critical systems.
- Practical expertise with Infrastructure-as-Code, with Terraform as a mandatory skill (CloudFormation
experience is an added advantage).
- Experience building and managing infrastructure to support AI/ML or agentic AI applications (e.g.,
integration with SageMaker, Bedrock, custom model serving, or similar).
- Solid understanding of networking concepts (VPC, subnets, routing, security groups, VPN, Direct
Connect, DNS, Transit Gateway, Inspection VPC).
- Strong background in Linux-based systems administration and shell scripting.
- Strong, hands-on experience with containerization and orchestration, specifically on Amazon
ECS/EKS or Kubernetes, including designing and operating containerized solutions in production.
- Knowledge of CI/CD tools and pipelines (e.g., GitLab CI, GitHub Actions, Jenkins, AWS
CodePipeline/CodeBuild).
- Good understanding of cloud security best practices, identity and access management, and
compliance considerations.
- Strong troubleshooting skills and ability to work in a fast-paced, collaborative environment.
Good to Have Skills
- Experience with AWS AI/ML services (SageMaker, Bedrock, Lex, Kendra, Comprehend) or othercloud AI platforms.
- • Familiarity with agentic AI frameworks, orchestration tools, or LLM application frameworks.
- • Experience with serverless architectures (Lambda, API Gateway, Step Functions).
- • Knowledge of data engineering components on AWS (Glue, Kinesis, Redshift, EMR, Athena).
- • Exposure to observability stacks and APM tools (Datadog, New Relic, Dynatrace, or similar).
- • Experience implementing cloud governance, tagging strategies, and FinOps practices.
- • Knowledge of security frameworks and standards (CIS Benchmarks, ISO 27001, SOC 2) and related
- tooling.
- • Background in Python or other scripting languages for automation and tooling development.
- Experience Required
- • 6 to 8 years of relevant experience in AWS infrastructure engineering or cloud platform engineering
- roles
Pay: ₹322,930.16 - ₹1,717,076.57 per year
Benefits:
- Paid sick time
- Paid time off
- Provident Fund
Work Location: In person
