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Mahdi Sarhangi

Data Scientist · AI Engineer · Building Scalable Intelligent Systems

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Profile

Data Scientist with a strong foundation in machine learning, data systems, and production-grade architectures.

I specialize in designing systems where models, data, and infrastructure work together — from research prototypes to deployed AI products.

Current focus:

  • Knowledge Graphs & Graph Neural Networks
  • Retrieval-Augmented Generation (RAG) systems
  • LLM-powered applications and agentic workflows
  • Scalable data pipelines and AI infrastructure

Experience

Data Scientist / AI Engineer — Argoman (Remote)
2024 – Present

  • Built Aether, an expertise discovery system combining knowledge graphs and LLMs
  • Designed and deployed RAG pipelines with structured + unstructured data
  • Developed scalable backend services using FastAPI, PyTorch, LangChain
  • Implemented data orchestration pipelines (Airflow, Docker)

Full-Stack Engineer / Co-Founder / CTO — UNIJOB
2023 – 2025

  • Architected and launched a production-scale job platform
  • Built cross-platform applications (Flutter) and backend systems (Python, Firebase)
  • Designed real-time systems, authentication flows, and scalable infrastructure
  • Researched and integrated AI-driven recommendation features

Software Engineer — Arçelik Global (Remote)
2022 – 2023

  • Developed Generative AI-powered enterprise tools
  • Built full-stack systems (Flutter, FastAPI) deployed on cloud infrastructure

Selected Work

Breast Cancer Prognosis with Graph Neural Networks

  • Designed predictive models using gene expression data + GNNs
  • Applied transfer learning and semi-supervised learning
  • Focused on personalized risk prediction in healthcare AI

Aether — Knowledge Graph + LLM System

  • Built an expertise discovery engine using Neo4j + RAG pipelines
  • Integrated structured knowledge with LLM reasoning

Multi-Agent Healthcare Simulation

  • Modeled hospital workflows using agent-based systems
  • Designed decentralized, event-driven coordination

Technical Strengths

AI / ML

  • Deep Learning, Representation Learning
  • Graph Neural Networks
  • Retrieval-Augmented Generation (RAG)
  • LLMOps & Agentic Systems

Data & Infrastructure

  • ETL / ELT Pipelines
  • Knowledge Graphs (Neo4j)
  • SQL / NoSQL Systems
  • Workflow Orchestration (Airflow)

Engineering

  • Python, FastAPI
  • PyTorch
  • Docker
  • System Design & Scalable Architectures
  • Flutter / Dart

Education

M.Sc. Computer Engineering — Ege University
Focus: Graph Neural Networks in Healthcare AI

B.Sc. Computer Engineering — Ege University


Contact


Notes

  • Interested in AI for healthcare, knowledge systems, and real-world ML deployment
  • Open to collaborations and impactful engineering challenges

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