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ccakirr/README.md

Caner Çakır

Software Developer · AI & ML Engineering · Quantitative Development

👨‍💻 About Me

While studying Software Development at Istanbul Aydın University, I am building my focus around AI Engineering, machine learning, and modern software architectures. Rather than treating models or intelligent components as isolated pieces, I am particularly interested in integrating them with backend systems, APIs, data pipelines, evaluation processes, and product layers to build end-to-end AI systems — from classical ML to LLMs, RAG, and agentic applications.

For me, successful AI engineering is not only about achieving strong model performance. It is about making AI systems measurable, reliable, scalable, and usable as part of real software products. This is why I am especially interested in the intersection of artificial intelligence and software engineering.

Alongside my technical education, I study Economics at Istanbul University, which gives me an additional perspective on data, decision-making, economic systems, and market dynamics. This interdisciplinary background also supports my interest in quantitative finance and data-driven systems, where software, mathematics, and economics intersect.

My experience at 42 Istanbul strengthened my foundations in systems programming, algorithmic problem-solving, and low-level thinking. It also shaped the way I approach technology: not only understanding how to use tools, but also questioning how they work underneath.

I enjoy moving across disciplines, learning new technologies, and connecting different layers of engineering to turn ideas into working systems.

In the long term, my goal is to combine artificial intelligence, strong software engineering fundamentals, systems thinking, and analytical reasoning to build scalable, intelligent systems with real-world impact.

🎯 Focus Areas

  • 🤖 Artificial Intelligence & AI Engineering
  • 🧠 Machine Learning & Deep Learning
  • 🕸️ Neural Networks
  • 🎮 Reinforcement Learning
  • 💬 Generative AI, RAG & Agentic Systems
  • ⚙️ MLOps & Production AI
  • 🧩 Backend & Full-Stack Engineering
  • 📊 Quantitative Finance & Algorithmic Trading

🛠 Tech Stack

Languages

AI & Data

Backend & Frontend

Infra & Tooling


🎓 Education

  • İstanbul Aydın University — Software Development (2023 – Present)
  • İstanbul University — Economics (2026 – Present)
  • 42 İstanbul — Core Curriculum (2025 – 2026)

📍 Location

Istanbul, Türkiye


🤝 Open To

  • AI Engineering & LLM integration roles
  • ML Engineering & Data Science roles
  • Backend engineering (Python-heavy)
  • Quantitative / fintech engineering
  • Internships, freelance & technical collaboration

Building at the intersection of AI engineering, data, and quantitative development.

Pinned Loading

  1. codepath_ai codepath_ai Public

    An end-to-end AI career intelligence platform combining ML-powered salary estimation, peer skill benchmarking, technology recommendations, and personalized career roadmaps.

    Jupyter Notebook

  2. pairs-trading-agent pairs-trading-agent Public

    LLM-powered quantitative research agent for statistical arbitrage — runs cointegration tests, hedge-ratio estimation, pair scanning, event-driven backtests and live z-score signals as tools, ground…

    Python

  3. TradeMirror TradeMirror Public

    Privacy-first trading journal and behavioral risk engine — FastAPI backend with rule-based plan-violation and revenge-trading detection, PostgreSQL persistence, and an ML classifier estimating blow…

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  4. jarvis jarvis Public

    Voice-enabled AI data analysis assistant — an LLM agent that inspects, cleans, transforms and analyzes CSV datasets through natural-language voice or text commands, saves each transformation as a n…

    TypeScript

  5. statarb_lab statarb_lab Public

    A Python research lab for forex pairs trading using Engle-Granger cointegration, OLS hedge ratios, z-score mean reversion, walk-forward validation, and pair scanning.

    Python

  6. backtest_engine backtest_engine Public

    Event-driven Python backtesting engine featuring look-ahead-safe execution, fractional Kelly position sizing, portfolio accounting, and performance analytics.

    Python