Platform Engineer

Build, maintain, and improve production cloud infrastructure using infrastructure as code.

Operate Kubernetes-based environments, including workloads, networking, ingress, autoscaling, and identity integration.

Improve CI/CD pipelines for application, worker, service, and infrastructure deployments.

Support core platform services across databases, caches, object storage, container registries, secrets, load balancing, and networking.

Help operate workflow orchestration, worker infrastructure, async processing systems, and agentic analysis pipelines.

Improve observability across logs, metrics, traces, alerts, dashboards, and production debugging workflows.

Harden infrastructure security across IAM, secrets handling, network boundaries, workload identity, and deployment permissions.

Partner with product and backend engineers working across frontend, backend, data, and AI-assisted security workflows.

Build and maintain internal platform tooling, runbooks, automation, and deployment workflows.

Debug production issues across infrastructure, application, worker, and database layers.

Help create the foundation that lets engineering teams ship quickly, safely, and confidently.

Strong experience operating cloud infrastructure in production.

Strong infrastructure-as-code experience, including modules, remote state, provider configuration, and safe change management.

Hands-on Kubernetes experience in production environments.

Familiarity with Helm, ingress controllers, load balancers, Kubernetes networking, service accounts, and workload identity patterns.

Experience operating PostgreSQL or similar relational databases, including availability, migrations, backups, and performance basics.

Comfortable debugging distributed systems using logs, metrics, traces, and cloud-native tooling.

Strong security instincts around IAM, secrets, network exposure, least privilege, and production access.

Ability to write useful automation in Python, TypeScript, Bash, or similar languages.

Good judgment around production changes, CI/CD safety, rollout strategy, incident response, and operational tradeoffs.

Ability to work in a fast-moving startup environment with ownership, autonomy, and good judgment.

AWS

Kubernetes

Terraform

Helm

GitHub Actions

Docker

Node.js

Bun

Python

TypeScript

PostgreSQL

Redis

Lexical, semantic, and hybrid search

Vector search

Graph-based data modeling

Workflow orchestration systems

Data ingestion and indexing pipelines

Experience with workflow orchestration systems.

Experience operating multi-environment SaaS infrastructure.

Experience with Kubernetes autoscaling, node groups, workload isolation, and production reliability patterns.

Experience with AI infrastructure, security products, developer tools, or code analysis systems.

Familiarity with TypeScript monorepos and Python service environments.

Experience supporting systems that process code, security findings, infrastructure metadata, or customer-specific context.

Deployments become safer, faster, and easier to reason about.

Infrastructure changes are well-tested, observable, and reversible.

Engineers have better tooling and fewer platform blockers.

Production systems are more reliable, secure, and understandable.

The platform helps Pi move quickly while preserving the trust, isolation, and control customers expect from a security product.