Ömer Faruk Aydın

AI Engineer & System Architect | Tech Lead

Architecting scalable AI infrastructure, leading technical execution, and bridging ML models with high-performance products.

Istanbul, Türkiye
[ Download Full CV ]
01. Executive Summary

AI Engineer and Technical Architect with a proven track record of leading engineering execution, designing high-throughput data systems, and scaling early-to-mid stage startup operations. As Technical Lead & Co-Founder at SEOEM.CO, I orchestrate backend architectures, direct enterprise client deliveries, and guide technical teams.

In parallel, I drive applied AI research within the Yıldız Technical University Space Physics Group, engineering automated pipelines for continuous space weather modeling. My focus lies at the intersection of robust system design, LLM orchestration, and strategic engineering management—transforming complex technical challenges into scalable, high-impact products.

02. Technical Architecture & Core Competencies

Architecture & Infrastructure

High-Throughput ETL System Design PostgreSQL Docker Linux / CI/CD Async Workflows

Generative AI & Data Science

Gemma 3 / Ollama LLM Orchestration PyTorch / TensorFlow Time-Series ML Diffusers / LoRAs

Engineering Leadership

Technical Delivery Stakeholder Management Proposal & Budgeting Mentorship (100+ Engineers)

Core Languages & Frontend

Python (Expert) Vanilla JS (Zero-Dep) SQL C++ Golang
03. Leadership & Professional Experience

Technical Lead & Co-Founder

Oct 2023 — Present

SEOEM.CO | Istanbul

  • Architect end-to-end backend workflows and high-throughput data models for internal products and enterprise clients.
  • Lead technical execution and client delivery for enterprise platforms, overseeing scoping, architecture design, and final launch.
  • Delivered accredited technical instruction and frontend engineering programs at Yıldız Technical University.
  • Provide strategic AI and data infrastructure consulting to academic research teams and institutional clients.

AI Researcher — Space Physics

2019 — Present

YTU Space Physics Research Group

  • Designed automated, high-throughput data collection and processing infrastructure handling global GNSS sensor feeds (Python, PostgreSQL).
  • Built ML-driven predictive pipelines for time-series forecasting and real-time ionospheric disturbance monitoring.
  • Served as Principal Investigator on space-weather modeling projects; co-authored peer-reviewed publications (IEEE, TFD).

Data Scientist & ML Engineer

2025 — Present

TÜBİTAK POLAR 1001 — Antarctica Ionospheric Monitoring

  • Engineered real-time data ingestion pipelines for low-cost GNSS receivers deployed on Horseshoe Island, Antarctica.
  • Developed ML performance analysis workflows for polar space-weather dynamics.

Technical Instructor & Mentor

Past

Deneyap Türkiye

  • Mentored 100+ students in software engineering, system design, and algorithms.
  • Awarded Best Presentation Award at Teknofest (KATAY Project).
04. Key Systems & Architecture

VectorOSINT — Autonomous AI Analysis Platform 2025 - Present

System Architect & Lead Developer

Designed an autonomous OSINT platform powered by an LLM-driven analysis pipeline (Gemma 3 via Ollama). Built an automated ingestion engine continuously processing 20+ live data streams, handling orchestration, feature extraction, real-time alert dispatch, and automated synthesis.

LLM_Orchestration Gemma_3 Async_Ingestion Realtime_Alerts

Enterprise Luxury E-Commerce Platform 2024

Lead UI/UX & Frontend Architect (via SEOEM.CO)

Architected and engineered the front-end platform for a major luxury apparel brand. Designed the UI/UX from scratch and implemented a zero-dependency (Vanilla JS/CSS) architecture to guarantee sub-second page loads, maximum conversion efficiency, and flawless responsiveness.

Vanilla_Stack UI/UX_Architecture Performance_Optimization

Global Space Weather Predictive Pipeline 2024 - 2025

Principal Investigator & Pipeline Architect

Engineered an end-to-end regression and time-series forecasting model for global ionospheric disturbance prediction (TEC). Built scalable data cleansing and feature extraction workflows trained on massive historical GNSS datasets.

Time_Series_ML Big_Data_ETL Predictive_Modeling
05. Publications & Academic Background

Peer-Reviewed Output

  • Calculation of Ionospheric Disturbances of Earthquakes in Europe with TEC Anomalies Published in: IEEE
  • Modeling Space Weather Parameters With Machine Learning: Total Electron Content Forecasting Published in: TFD41 Congress / Int. TPS Congress
  • Preliminary Analysis for Polar Ionosphere Monitoring Based on GNSS-Based TEC Measurements at Horseshoe Island Published in: National Polar Sciences Symposium

Academic Background

M.Sc. in Physics (Ongoing)
Yıldız Technical University, Istanbul
Thesis: AI in Global Space Weather Forecasting (Mid-Latitude & Polar Dynamics)
2024 - Present
B.Sc. in Physics
Yıldız Technical University, Istanbul
2015 - 2024