Kadıköy, Istanbul, Türkiye
I am passionate about Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), Generative AI, cloud computing, and deploying scalable AI solutions. My expertise spans natural language processing (NLP), predictive analytics, AI agents, and workflow automation, allowing me to build intelligent AI-driven systems that enhance automation, decision-making, and deliver actionable insights. I have hands-on experience developing autonomous AI agents. My technical skills include Python, Numpy, Pandas, TensorFlow, OpenCV, Hugging Face Transformers, Scikit-learn, n8n, workflow automation, and MCP server development, which I use to create and deploy advanced AI systems and intelligent automation pipelines. Beyond technical expertise, I am highly skilled in Agile methodologies, project management, and technical documentation, enabling effective collaboration in cross-functional teams. My experience with data structures, algorithms, statistical analysis, and AI model optimisation ensures that I develop high-performance AI solutions that are both innovative and practical. My Expertise & Focus Areas: • LLMs & NLP: GPT Fine-Tuning, BERT, Named Entity Recognition (NER), Sentiment Analysis • Big Data & AI Analytics: Feature Engineering, Vector Databases (ChromaDB, FAISS), AI-driven Analytics, Context Engineering • AI Agents & Automation: Autonomous AI Agents, Model Context Protocol (MCP), Multi-Agent Systems, Intelligent Workflow Orchestration, n8n • AI Ethics & Responsible AI: Fairness, Bias Mitigation, Model Interpretability • Software Development: Python, Java, C++, SQL, Data Structures, OOP I am always looking to push the boundaries of AI innovation, cloud scalability, and real-world impact. I thrive on solving complex challenges, optimising AI-driven automation, and exploring new frontiers in machine learning.
•Enabled real-time, data-driven model selection by developing a full-stack LLM benchmarking platform that allowed the company to objectively select the highest-performing and most cost-effective models for specific business needs. •Provided precise, business-aligned model comparisons by engineering a production-grade evaluation framework featuring dynamic, custom-weighted scoring and an interactive analytics UI. •Guaranteed semantic accuracy and business relevance in critical domains by building robust evaluation pipelines; leveraged sentence-transformers and embeddings. •Significantly accelerated the evaluation lifecycle and minimized manual testing efforts by automating the full AI benchmarking process; integrated no-code tools like n8n.
•Developed and optimised machine learning (ML) and deep learning models for text classification and brand name recognition using frameworks such as TensorFlow, PyTorch, and Scikit-learn, improving the accuracy of AI-driven consumer insights. • Built and maintained end-to-end natural language processing (NLP) pipelines, including data preprocessing, tokenisation, and feature extraction to analyse and interpret open-ended survey responses. • Worked with large-scale datasets, performing data cleaning, feature engineering, data augmentation, and handling missing values using Python, Pandas, and NumPy for efficient model training and data manipulation. • Applied Natural Language Understanding (NLU) techniques, including Named Entity Recognition (NER) and sentiment analysis to identify emotional tone (positive, neutral, negative), subject-specific contexts, and consumer intentions from unstructured, open-ended text.
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