Platform Engineer (AI Systems)

05 years experience The team

This team builds, operates, and owns the production platform that runs our AI agents. The foundation is a cloud-agnostic, secure-by-default modern CNI Kubernetes stack. What this team ships, this team operates. There is no handoff the team owns the full product lifecycle, including design, security, packaging, release, and operations.

What you'll work on

- Build the Kubernetes production stack: ingress, SSO, databases, object storage, caching, secrets, and telemetry, shipped as one versioned package that installs with a single command.
- Extend the self-service layer: custom resources and controllers that auto provisions application teams scoped databases, buckets, caches, and identity realms with least-privilege credentials.
- Build and harden the sandboxed execution services that run untrusted, model-generated code for AI and data workloads: process isolation, resource budgets, durable session workspaces.
- Build the agent runtime: the trusted service that holds the LLM provider connection, streams conversation turns, and dispatches every tool execution into the sandbox rather than running it in-process.
- Write the services and tooling around the platform in Python or Go: HTTP APIs, executors, Kubernetes controllers, bootstrap and release automation.
- Prove the security model: build telemetry, alerting,



and automated checks that assert the isolation controls against live deployments.
- On-call responsibilities: team members take turns being on-call for production issues. The rotation is 1-6 (1 week on and 6 weeks off).

Requirements

- 2-5 years of skilled or project experience writing systems software or infrastructure as code.
- Strong fundamentals in algorithms.
- Demonstrated depth in Go or Python.
- Demonstrated depth in Kubernetes or Terraform.
- Academic excellence.

We understand that this is a junior engineering role, and demonstrated depth in one skill from each pair (e.g. Kubernetes and Python) is what passes the interview. 90-day success criteria

By day 30:

- Run the current platform locally: bootstrap the stack, deploy the sandbox and agent runtime on it, and onboard the example app through the self-service claims.
- Ship a first change to production small is fine; the point is completing one full design-review-release cycle.

By day 60:

- Own a backlog item end to end: design, implementation, release, and the operational follow-up, with review from the team.
- Land a change in at least two of the three products (stack, sandbox, agent runtime).

By day 90:

- Carry a quarterly backlog item without day-to-day supervision.
- Complete an on-call shadow week and join the 1-6 rotation.
- Run the isolation checks against a live deployment and explain what each control prevents.

📌 Platform Engineer (Hyderabad)
🏢 MediaMint
📍 Hyderabad