Vienna, Austria
Mehdi Bayat
Senior Full-Stack Engineer · Distributed Systems · Cloud & AI
I build real-time, scalable software and use AI to accelerate engineering without outsourcing engineering judgment.
Featured Engineering
LivePulse
Real-Time Sports Intelligence Platform
A continuously running, event-driven sports data platform built on real football/soccer data — not a simulator. LivePulse ingests live match data from a real external provider (API-Football), detects meaningful changes, publishes internal domain events, and pushes real-time updates to the browser over WebSockets, backed by PostgreSQL for durable history, Redis for live state, and Kafka as the internal event backbone.
Status
Live and running against real data, not a local-only demo: ingestion, change detection, Kafka, WebSockets, observability, and automated testing are all built and verified against real API-Football and football-data.org data. An AI-features phase is deliberately deferred and kept separate from the core pipeline.
One Engineering Story
Build → Understand → Validate
BUILD
LivePulse
The real distributed system: event-driven ingestion, Kafka, Redis, PostgreSQL, and a WebSocket gateway pushing live updates to the browser.
UNDERSTAND
Architecture Lab
An interactive companion exploring the system component-by-component: real event flows, 8 accepted ADRs, and the actual production incident, not a marketing diagram.
VALIDATE
Scaling Demo
A real load test against two unmodified LivePulse backend processes, measuring whether the WebSocket fan-out design actually holds up across instances.
One coherent engineering case study — build the system, explain the architecture, and measure the claims.
Recruiter View
Engineering Evidence
Instead of a skills list, here is what each capability is actually backed by.
| Capability | Evidence |
|---|---|
| Distributed systems | LivePulse's event-driven ingestion → change detection → Kafka pipeline |
| Real-time systems | WebSocket gateway architecture, validated by the Scaling Demo's real fan-out measurements |
| Messaging | Kafka topics and consumer groups (LivePulse), RabbitMQ event bus (Sportradar) |
| Distributed state | Redis cache-aside and pub/sub design with documented failure modes (ADR-004) |
| Reliability | A real production incident investigated and fixed — the WebSocket subscribe race |
| Architecture | 8 accepted ADRs plus the interactive Architecture Lab exploring them |
| Testing | Unit, integration, and Playwright E2E tests across LivePulse and TalentMatch, wired into CI |
| Scalability | Scaling Demo — measured, not assumed, fan-out results across two real instances |
| Cloud | AWS ECS/Fargate, DynamoDB, SQS/SNS, Cognito, CDK/Terraform used in production at Sportradar and in TalentMatch's deployment config |
| AI Engineering | Production RAG pipeline on AWS Bedrock (Sportradar); AI-assisted implementation workflow across personal projects |
Also Built
Other Projects
TalentMatch
Production-oriented job matching backend
A modular-monolith backend for publishing jobs and matching candidates: a synchronous Fastify API plus an asynchronous BullMQ worker, with MongoDB as the source of truth, OpenSearch as a rebuildable read model, deterministic candidate scoring, idempotent applications, and a full AWS ECS/Fargate deployment configuration.
Architecture Lab
Interactive exploration of the LivePulse architecture
A companion Next.js app that walks through LivePulse component-by-component: the real event flows, the 8 accepted ADRs, the real production incident, and measured-vs-projected scaling — built to make the reasoning behind LivePulse inspectable, not just the code.
Scaling Demo
A real load test for one specific architectural claim
A standalone tool that runs two real, unmodified LivePulse backend processes against shared Redis and measures whether the WebSocket gateway's fan-out design actually delivers updates across both instances — closing a gap LivePulse's own engineering review named honestly.
Technology
Working Stack
Languages
Frontend
Backend
Distributed Systems
Cloud & Infrastructure
Data
AI Engineering
Testing & Quality
Observability
How I Engineer
Engineering Philosophy
- Evidence over assumptions
- Architecture proportional to requirements
- Explicit trade-offs
- Automated verification
- Failure-mode thinking
- Observable systems
- Measured performance, not claimed performance
- Human-owned engineering decisions
AI-Augmented Engineering
Where AI fits in how I build
AI tools are part of my day-to-day workflow — implementation acceleration, test generation, refactoring support, code review assistance, debugging, and documentation. LivePulse and its Architecture Lab were built with heavy AI-assisted implementation, which is exactly why both projects document their engineering process, ADRs, and real incidents so openly: the reasoning has to survive scrutiny independent of who typed the code.
AI accelerates implementation; engineering judgment — what to build, which trade-offs to accept, how to verify a claim — remains human-owned.
Get in Touch