Riad Eita

Riad Eita · reading mode

Everything, as one page.

The home page lets you explore this as a system. Here it is in reading order. Back to the system

Role
Software & AI Engineer
Works at
Siemens (via Eviden), 2022 to present
Based in
Berlin, Germany
Focus
AI Engineering, Enterprise Integration, System Architecture
Core technologies
React, TypeScript, Java, Python, REST APIs, Agentic AI, MCP, OpenShift, Kubernetes
Languages
German, Arabic, English
Contact
riad.eita@icloud.com

01About

Most of my work happens between systems.

A language model becomes useful once it can reach the right data, with the right permissions, inside the tools people already use. A deployment is finished when it behaves the same way on every server. A sign-in works when nobody notices it crossing from one system to the next.

That in-between space – interfaces, contracts, pipelines, identity – is where I spend most of my time: in Java and TypeScript, in Python services, on container platforms, and in the architecture decisions that tie them together.

02Featured system

Embedded AI Assistant

An AI assistant inside an enterprise engineering platform

  1. IThe problem

    Engineering knowledge is spread across specifications, tickets, documents and the tools around them. Finding the right context – and then acting on it – means switching views, writing queries and leaving the tool.

  2. IIThe system

    Bring the assistant to the data instead of the data to another application: an assistant that lives inside the engineering platform, understands where it is opened, and works through approved tools rather than open-ended access.

  3. IIIThe architecture

    Select a component, or follow a request through the system. The platform boundary marks what runs on the container platform.

    Follow a request

  4. IVThe implementation

    Agentic AI & MCP
    MCP-based tool integrations / Skills architecture / Tool discovery / Approved operations / Contextual execution
    Retrieval & knowledge
    RAG architecture / Vector retrieval / Ingestion pipelines / FastAPI services / ChromaDB
    Enterprise integration
    Platform APIs / Server extensions / Identity integration / Authentication / Networking / Deployment environments
    Frontend
    Streaming responses / Conversation management / Markdown rendering / Configuration / Model selection / Import / export
    Platform engineering
    OpenShift / Kubernetes / Kustomize / CI/CD / Configuration management / Troubleshooting

  5. VEngineering challenges

    1. 01

      Streaming through an enterprise stack

      Token-by-token answers from the model, through a Java backend, into a React interface running inside a server-side platform.

    2. 02

      Permissions stay with the user

      Tools act through the platform's APIs and identity – no super-user in between.

    3. 03

      Deciding what the model sees

      Retrieval results, project context and skill instructions have to fit a context budget – and only what is needed goes in.

    4. 04

      Running it

      Containerized services on OpenShift, per-environment configuration with Kustomize, pipelines with tests and quality gates, enterprise authentication in front.

  6. VIThe result

    An assistant that answers with the project's own context and carries out approved operations in the platform – without asking engineers to leave the tool they already work in.

StackJava · LangChain4j · React · TypeScript · Python · FastAPI · ChromaDB · PostgreSQL · MCP · OpenShift · Kubernetes · Kustomize

03Selected work

Other systems, other seams.

04.1 Platform Engineering

Extension delivery across a server landscape

Siemens (via Eviden) · platform extensions

  1. 01Resolve versions
  2. 02Fetch artifacts
  3. 03Roll out as a matrix
  4. 04Run again – nothing changes
Problem
Enterprise extensions have to reach a large landscape of servers in compatible versions – without manual steps that drift apart over time.
Approach
Automated pipelines that resolve dependencies and versions, fetch artifacts, and roll out through matrix deployments – written to be idempotent, so a repeated run leaves correct servers untouched.
Role
Designed and built the deployment pipelines.
Outcome
Rollouts that are repeatable and traceable instead of hand-made.
Technologies
Jenkins · GitHub Actions · Ansible · Matrix deployment · Artifactory · SVN · Semantic Versioning

04.2 Enterprise Systems

Dependency & metadata services

Siemens (via Eviden) · container platform

  • Repositories
  • Artifact storage
  • Servers
  • Pipelines

One API

Problem
Which extension depends on what – and which version runs where – is scattered across repositories and servers.
Approach
REST APIs and microservices on a container platform that resolve dependencies and aggregate metadata into one place pipelines and people can query.
Role
Developed the APIs and services.
Outcome
One place to ask instead of many places to look.
Technologies
REST API design · Microservices · OpenShift · Docker

04.3 Identity

Token exchange for single sign-on

Siemens (via Eviden) · internal tooling

Problem
Internal tools each authenticate on their own; people should not have to sign in to every one of them.
Approach
A secure token exchange that federates identity between systems – JWT, OAuth2 / SSO and GitHub App authentication – with secrets managed outside the code.
Role
Implemented the token exchange service.
Outcome
Moving between integrated tools without signing in again.
Technologies
JWT · Token federation · SSO / OAuth2 · GitHub Apps · Secrets management

04.4 Personal Project

Project Hub

Personal project · iOS and web

Project work – time, trips, expenses, appointments – ends up in five apps, and none of them produces the document needed at the end.

  1. L1Next.js interface
  2. L2Native bridge (Capacitor)
  3. L3SwiftUI · widgets · Siri
  4. L4Encrypted file in the user's folder
Approach
A local-first app: a Next.js interface in a native iOS shell with SwiftUI screens, widgets and Siri shortcuts. Data is encrypted on the device and stored in a folder the user chooses, such as iCloud Drive; project dossiers and travel expense reports are generated as PDFs on the device.
Role
Designed and built it.
Outcome
In TestFlight testing on iOS; the web version runs at projecthub.fs223.de.
Technologies
TypeScript · Next.js · Swift · SwiftUI · Capacitor · WidgetKit · App Intents · Web Crypto

projecthub.fs223.de

04Expertise

One stack, read top to bottom.

Technical expertise as the layers of a system. The large names carry the work; the small ones support it.

  1. L1ExperienceInterfaces
    • React
    • TypeScript
    • Next.js
    • Angular
    • Vite
  2. L2ApplicationSoftware engineering
    • Java
    • Python
    • REST APIs
    • C#
    • Microservices
    • Servlet development
    • Velocity templates
  3. L3AIAI & intelligent systems
    • Agentic AI
    • MCP
    • LLM integration
    • RAG
    • Vector databases
    • AI tooling & automation
  4. L4IdentityIdentity & security
    • JWT
    • Token federation
    • SSO / OAuth2
    • GitHub Apps authentication
    • Secrets management
  5. L5PlatformPlatform & DevOps
    • OpenShift
    • Kubernetes
    • Docker
    • Kustomize
    • GitHub Actions
    • Jenkins
    • Ansible
    • Matrix deployment
    • GitHub Enterprise Server
  6. L6DataData & infrastructure
    • PostgreSQL
    • SVN
    • Artifactory
    • Dependency resolution
    • Semantic versioning
    • Idempotent deployments
  7. L7QualityQuality & testing
    • SonarQube
    • Unit testing
    • CI/CD quality gates
    • PDF generation

System architecture

Across every layer: how the parts meet, who owns which contract, and what happens when one of them fails.

05Journey

The scope keeps widening.

  1. 01

    Software

    One application

    Java, TypeScript, Python and C# – the craft of building a program that works, learned at DHBW and ATIW.

  2. 02

    Enterprise systems

    A platform many teams depend on

    Extending a large enterprise platform from the inside: server extensions, servlets, Velocity templates, REST APIs.

  3. 03

    AI integration

    Models inside existing tools

    LLMs, retrieval and MCP brought into the tools engineers already use – with their permissions, not around them.

  4. 04

    Platform engineering

    Everything that has to run

    Pipelines, container platforms and deployments: Jenkins, GitHub Actions, Ansible, OpenShift, Kubernetes.

  5. 05

    System architecture

    How the parts connect

    Identity, integration, deployment and data – designing the seams between systems, not only the systems.

  6. 06

    Solution architecturedirection

    Technology, organisation, people

    Where this is heading: shaping end-to-end solutions with the people who will use and own them.

Current roleSoftware & DevOps Engineer · Siemens (via Eviden) · 2022 – present

Building agentic AI solutions for an enterprise engineering platform, integrating Model Context Protocol (MCP) servers, LLM tooling and AI-driven automation to extend platform capabilities. Designing and building automated deployment pipelines that manage enterprise software extensions across large server landscapes. Developing REST APIs and microservices on container platforms with dependency resolution and metadata aggregation. Building React-based frontends integrated into enterprise tools. Implementing secure token exchange services for seamless SSO across internal systems. Working across the full stack: infrastructure automation, backend services, frontend interfaces and AI integration.

06Education

Two ways of reading a system.

Social sciences

  1. B.A. Political Science, Public Administration & Sociology

    FernUniversität in Hagen

    April 2026 – present

Technology

  1. Bachelor of Science (B.Sc.) – Applied Computer Science

    DHBW Mannheim

  2. Software Consultant

    ATIW – Siemens Professional Education

  3. Allgemeine Hochschulreife

    OSZ IMT

07Beyond the code

Systems are never only technical.

Every architecture encodes decisions: who can see what, who decides, who is left out. Institutions, laws and habits shape technology as much as technology reshapes them.

Studying political science and sociology next to engineering is my way of looking at the same systems from the other side – from the institutions and the people inside them.

  1. Technology

    • Philosophy of technology
    • AI ethics & society

  2. Institutions

    • Democracy & governance
    • Social structures & institutions

  3. Society

    • Sociology
    • Migration & identity

  4. Politics

    • Political theory
    • International relations
    • Middle Eastern studies

  5. Ethics

    • Philosophy
    • Ethics & critical theory

Questions, answered.

Who is Riad Eita?

Riad Eita (رياض عيطة) is a Software & AI Engineer based in Berlin, Germany, working at Siemens (via Eviden) since 2022. Riad designs and builds systems where software, AI and enterprise infrastructure meet: AI assistants with the Model Context Protocol (MCP) and retrieval, enterprise integration, deployment pipelines and system architecture.

What does Riad Eita work on?

Riad Eita's flagship project is an Embedded AI Assistant: an AI assistant inside an enterprise engineering platform, built with Java, LangChain4j, React, TypeScript, Python, FastAPI and more. Around it: extension delivery across a server landscape; dependency & metadata services; token exchange for single sign-on.

Which technologies does Riad Eita use?

Riad Eita works mainly with React, TypeScript, Java, Python, REST APIs, Agentic AI, MCP, OpenShift, Kubernetes. Supporting: Next.js, Angular, Vite, C#, Microservices, Servlet development, Velocity templates, LLM integration, RAG, Vector databases, AI tooling & automation, JWT, Token federation, SSO / OAuth2.

What is Project Hub?

Project Hub is a personal project by Riad Eita: a local-first app that keeps project work – time, trips, expenses, appointments – in one place and generates the documents needed at the end. Under the hood: a Next.js interface in a native iOS shell with SwiftUI screens, widgets and Siri shortcuts. Data is encrypted on the device and stored in a folder the user chooses, such as iCloud Drive; project dossiers and travel expense reports are generated as PDFs on the device. In TestFlight testing on iOS; the web version runs at projecthub.fs223.de.

Where did Riad Eita study?

Riad Eita's education: B.A. Political Science, Public Administration & Sociology – FernUniversität in Hagen (April 2026 – present); Bachelor of Science (B.Sc.) – Applied Computer Science – DHBW Mannheim; Software Consultant – ATIW – Siemens Professional Education; Allgemeine Hochschulreife – OSZ IMT.

How can I contact Riad Eita?

By email: riad.eita@icloud.com. Riad is based in Berlin and speaks German, Arabic, English.

Everything connects somewhere.

riad.eita@icloud.com