Zscaler
About this role
This seat builds and runs the enterprise cloud infrastructure that Zscaler's production AI and machine
learning workloads sit on. It is platform engineering rather than modelling: scalable, secure, highly
available AWS infrastructure (EKS, Lambda, ECS, VPC, IAM) built with Terraform following
infrastructure as code practice, GitLab CI/CD pipelines that automate build, test, security scanning
and multi environment deployment, and a centralised observability stack on Prometheus and Grafana.
You also drive platform governance so AI and data engineering teams can ship reliably and securely.
It sits inside the IT Data Strategy team. Apply if you want MLOps and platform infrastructure work
with real AWS depth, and note it is offered in Bangalore or Pune.
learning workloads sit on. It is platform engineering rather than modelling: scalable, secure, highly
available AWS infrastructure (EKS, Lambda, ECS, VPC, IAM) built with Terraform following
infrastructure as code practice, GitLab CI/CD pipelines that automate build, test, security scanning
and multi environment deployment, and a centralised observability stack on Prometheus and Grafana.
You also drive platform governance so AI and data engineering teams can ship reliably and securely.
It sits inside the IT Data Strategy team. Apply if you want MLOps and platform infrastructure work
with real AWS depth, and note it is offered in Bangalore or Pune.
Who this is for
Requirements. 8+ years of industry experience. Deep AWS infrastructure experience across EKS, Lambda,
ECS, VPC and IAM. Terraform and infrastructure as code practice including reusable modules, remote
state management and environment based blueprints. CI/CD pipeline ownership, with GitLab named.
Observability tooling, with Prometheus and Grafana named.
Responsibilities. Design, build and maintain scalable, secure and highly available AWS infrastructure
for AI and ML workloads using Terraform, following IaC best practice including reusable modules,
remote state management and environment based blueprints. Own and continuously evolve GitLab CI/CD
pipelines for AI platform services, automating build, test, security scanning and multi environment
deployment so releases are fast, reliable and repeatable. Architect a centralised observability stack
using Prometheus and Grafana. Drive infrastructure automation, observability and platform governance
so AI and data engineering teams can deliver reliably and securely.
Where it sits. The posting places this in the IT Data Strategy team. That is internal platform
engineering serving the company's own AI and data teams, rather than customer facing product work.
It is genuine infrastructure engineering rather than end user IT support, which is why it is in scope
here.
Location. Bangalore or Pune, both listed, which is worth noting since almost every role in this
edition is Bangalore only. Zscaler operates a hybrid model.
Who this is for. A platform or DevOps engineer with eight or more years who wants to specialise
toward AI infrastructure. The AWS and Terraform requirements are the hard ones; the AI part is about
supporting ML workloads rather than building models, so you do not need a machine learning background
to be credible here. Pune as an option makes this accessible if Bangalore does not suit you.
ECS, VPC and IAM. Terraform and infrastructure as code practice including reusable modules, remote
state management and environment based blueprints. CI/CD pipeline ownership, with GitLab named.
Observability tooling, with Prometheus and Grafana named.
Responsibilities. Design, build and maintain scalable, secure and highly available AWS infrastructure
for AI and ML workloads using Terraform, following IaC best practice including reusable modules,
remote state management and environment based blueprints. Own and continuously evolve GitLab CI/CD
pipelines for AI platform services, automating build, test, security scanning and multi environment
deployment so releases are fast, reliable and repeatable. Architect a centralised observability stack
using Prometheus and Grafana. Drive infrastructure automation, observability and platform governance
so AI and data engineering teams can deliver reliably and securely.
Where it sits. The posting places this in the IT Data Strategy team. That is internal platform
engineering serving the company's own AI and data teams, rather than customer facing product work.
It is genuine infrastructure engineering rather than end user IT support, which is why it is in scope
here.
Location. Bangalore or Pune, both listed, which is worth noting since almost every role in this
edition is Bangalore only. Zscaler operates a hybrid model.
Who this is for. A platform or DevOps engineer with eight or more years who wants to specialise
toward AI infrastructure. The AWS and Terraform requirements are the hard ones; the AI part is about
supporting ML workloads rather than building models, so you do not need a machine learning background
to be credible here. Pune as an option makes this accessible if Bangalore does not suit you.
Apply on company site
Opens job-boards.greenhouse.io, the employer's own application page. Applying is always free.