• TypeScript, Node.js, and Angular
• GraphQL (Apollo), Camunda, Kafka, Postgres, and AWS
• CI/CD, Terraform, and GitHub Actions

<aside>

I use AI to:

• clarify project architecture, remove confusion on hierarchies and integration flow

• plan design docs for desired updates and changes, clarify scope of modifications, identify edge cases and create testing and validation requirements </aside>

• dynamic AI workflows

• Write clean, testable code and automated tests every sprint

  rimo case study: testing automation

• Actively use agentic AI tools to accelerate development, catch gaps, and improve quality

  rimo case study: tRPC → GraphQL migration

• Collaborate with product, UX, and engineering teams to deliver high-quality software

  rimo case study: frontend optimization

stack:

Frontend

• Angular (SPA framework)
• TypeScript throughout

Backend / API layer

• Node.js + TypeScript services
• GraphQL via Apollo (likely Apollo Server on the backend, Apollo Client on the frontend) as the primary API layer between frontend and services

Workflow / orchestration

• Camunda — this is a strong signal they're running BPMN-based workflow orchestration for the loan origination process itself (loan applications typically move through many stages/approvals, which is exactly what Camunda is built for)

Messaging / eventing

• Kafka — event streaming between services, likely used to propagate state changes (loan status updates, underwriting events, etc.) across a distributed system

Data layer

• Postgres (explicitly "preferred") as the relational database

Cloud / infra

• AWS as the hosting platform (exact services unspecified, but for this kind of stack you'd typically expect ECS/EKS or Lambda, RDS for Postgres, MSK or self-managed Kafka, and API Gateway)
• Nice-to-haves suggest CI/CD is not fully mature yet — Terraform and GitHub Actions are listed as pluses rather than requirements, implying infra-as-code and automated pipelines may be partial or in progress

Dev tooling / AI-assisted workflow