Akash Ramesh Full Stack Software Engineer
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Akash Ramesh

RMIT Master of IT graduate with hands on enterprise experience at Deloitte, building backend microservices for a global banking client. Also has real product ownership experience, having delivered a live B2B SaaS platform to a paying customer.

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About

I work across the backend with Java and Spring Boot and C# and ASP.NET Core, paired with React on the frontend. My experience spans the full delivery lifecycle, from designing REST APIs to deploying containerised services with Kubernetes. I also set up CI/CD pipelines, always with an eye on security and solid test coverage.

Beyond my professional role, I was Design Lead and backend engineer on a multi tenant B2B SaaS capstone during my Masters. It was delivered to a real paying client. I use AI assisted tools like GitHub Copilot and Claude Code to move faster, and still own the architecture and code quality myself.

What I'm looking for

I am currently open to junior and graduate full stack software engineering roles in Melbourne. I bring enterprise backend experience from Deloitte, real product ownership from shipping a live SaaS platform to a paying client, and a long term goal of growing into technical leadership and architecture. If you are hiring or know someone who is, I would love to connect.

Let's connect
Projects
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Projects

Search Sensei

Live

Multi tenant SaaS portal wrapping an enterprise search API, shipped to a paying client.

  • Stripe metered billing across three usage dimensions with idempotent usage reporting to prevent double counting
  • Keycloak 26 Organisations for multi tenant isolation, active_tenant claim resolved on every backend request
  • ASP.NET Core backend proxy pattern keeps BookStack and Zammad PATs server side, never exposed to the browser
  • Onboarding reduced from manual provisioning to under 5 minutes self service
  • Shipped v1.0.0 to a live paying client
Stack
ASP.NET Core 8React 18TypeScript
Integrations
ServiceRole
Keycloak 26Identity and multi tenant org management
StripeCheckout, subscriptions, metered billing
BookStackKnowledge base via backend proxy
ZammadSupport ticketing via backend proxy
Azure App ServiceHosting for containerised services

Customer Income Capture Microservice

Live

Backend microservice for direct income capture at card activation for a global banking client.

  • Replaced a 3 to 6 month manual review process with real time income capture at the point of card activation
  • Reached 61% customer adoption in the initial release
  • Cut external data vendor costs by 20% and lifted offer conversion by 12%
  • Extended existing Hadoop and Hive pipelines to feed captured income data to Tableau dashboards without new tooling
  • Apigee gateway handled auth and rate limiting, keeping the microservice focused on business logic
Stack
JavaSpring BootMongoDBKubernetes
Integrations
ServiceRole
ApigeeAPI gateway, auth, rate limiting
JenkinsCI/CD build and deployment pipelines
Hadoop/HiveBatch pipeline to enterprise datalake
AWS ECSContainer hosting post PCF migration
TableauBusiness analyst dashboards via datalake

Orthoneo

Staging

Freelance migration from a no code builder to a self hosted WordPress CMS for a healthcare practice.

  • Full end to end ownership: DNS, hosting setup, WordPress installation, theme build, plugin development, and client handover
  • Custom theme and plugin set built so non technical staff can manage all content without developer involvement
  • Migration carried zero additional hosting cost by running WordPress on the existing VentraIP plan
  • Elementor page builder layer gives staff layout control within brand constraints set by the custom theme
  • Next phase scoped: HotDoc booking integration and Claude API powered FAQ chatbot
Stack
WordPressPHPElementor
Integrations
ServiceRole
VentraIPShared hosting, DNS, cPanel management
ElementorPage builder for staff layout control
HotDocOnline booking integration (planned)
Claude APIAI powered FAQ chatbot (planned)

Prosopo

In progress

iOS app for building and reusing AI personas as structured system prompts.

  • Temperature computed from tone base value plus a consistency slider: final temp = base + (consistency - 0.5) x 0.4, clamped to 0 to 1
  • Six tone presets (Precise 0.1 to Friendly 0.6) map user intent to LLM temperature without exposing raw values
  • OpenAI used to generate structured JSON persona suggestions from plain language descriptions, decoded directly into the Persona model
  • Two tier persistence: SwiftData for local drafts, Firestore for cloud sync per authenticated user
  • WidgetKit extension reads favourite persona from App Group UserDefaults for instant home screen access without a network call
Stack
SwiftSwiftUISwiftData
Integrations
ServiceRole
Firebase AuthSign in with Apple and Google Sign In
FirestoreCloud sync for finished personas per user
OpenAI APIPersona suggestion and prompt generation
WidgetKitHome screen widget for quick persona access
App GroupsShared UserDefaults between app and widget