Hello! I'm Ece Dalpolat, a dedicated AI Engineer and data science enthusiast currently pursuing a Master's degree in Information Technologies. I hold a Bachelor's degree in Software Engineering from Kırklareli University and am actively expanding my expertise in data science and machine learning at Işık University. In my current role as an AI Engineer, I work extensively with Large Language Models (LLMs), semantic search, and vector databases to build intelligent systems capable of contextual understanding and natural language interaction. My responsibilities include developing and optimizing deep learning models, integrating external APIs, and deploying AI services in scalable and containerized environments using Docker and Kubernetes. I have a strong foundation in modern AI tools and frameworks including FastAPI, DSPy, ANTLR, and LangChain. My work emphasizes efficiency, modularity, and explainability in AI systems. I am passionate about building innovative solutions that bridge the gap between raw data and real-world applications. My commitment to continuous learning is reflected in the diverse projects I've contributed to, certifications I've earned in AI and ML, and my active participation in technical communities. I thrive in collaborative environments where I can experiment, learn, and deliver high-impact solutions.
June 2025 - Present:
At Qkare, I work as an AI Engineer focusing on the development and deployment of intelligent systems powered by Large Language Models (LLMs). I lead initiatives such as CVAnalyzerAI, a robust resume analysis platform that leverages external APIs and custom LLM pipelines (using DSPy, FastAPI, and Hugging Face) to evaluate candidate suitability and job matching.
My responsibilities include designing modular, scalable architectures for AI services, integrating MCP (Model Context Protocol) to ensure compatibility with various LLM clients (e.g., Claude, OpenAI), and implementing ANTLR-based parsers to extract structured information from unstructured text. I also manage containerization and deployment using Docker and Kubernetes, ensuring production-readiness and CI/CD compatibility through tools like GitLab and Docker Desktop.
My role bridges model engineering and full-stack AI deployment—transforming prototypes into real-world AI services that enhance decision-making in recruitment workflows.
January 2025 - Present: Conduct weekly lectures and deliver presentations on data science and machine learning (ML) topics. Guide and evaluate participants during hands-on project implementations. Support learning processes
January 2024 - June 2025: Assist in faculty development and teach software lab courses. Worked on supervising and helping students develop their software projects, especially focusing on data science and deep learning projects.
January 2024 - June 2025: Data Collection and Processing: I collect and process data related to honey value chains and apply various engineering techniques to ensure quality data. Model Development: I develop and optimize models using deep learning and machine learning, effectively using tools such as Python, TensorFlow and scikit-learn. API Integration: I perform API integrations to ensure that the models are compatible with other systems.
November 2023 - January 2024: Automation Testing: Using test automation tools (e.g. Selenium, TestComplete), I developed automation tests to speed up testing processes and increase repeatability. By writing test scripts, I increased the efficiency of testing processes and ensured the quality of the software. Manual Testing: Using manual testing methods, I tested software applications to ensure error-free and functional software products that meet user requirements. I identified and reported defects and proposed solutions. Knowledge and Skills: I gained in-depth knowledge of software testing strategies, test case generation, defect reporting and software development life cycle (SDLC). I contributed to improving testing processes and enhancing software quality.
October 2023 - November 2023: ava, Eclipse/IntelliJ IDEA, SQL. Using these tools, I created effective and scalable software solutions and gained experience in software development processes.
July 2022 - September 2022: I managed the application development process using ASP.NET MVC, C#, SQL Server, Visual Studio. I developed team communication and collaboration skills to work efficiently in software projects.
Description: CVAnalyzerAI is an intelligent resume analysis platform built with Model Context Protocol (MCP) and multiple AI services. It analyzes CVs to extract strengths, weaknesses, and job-fit insights using LLMs and structured parsing.
Technologies: Python, FastAPI, Docker, Kubernetes, ANTLR, DSPy, Hugging Face, Tavily API, Brave API, Firecrawl
What I Did: Built an end-to-end AI system with containerization and MCP support, integrated multiple external APIs, and developed a robust FastAPI backend with caching and logging.
Impact: Helps HR teams reduce review time and gain AI-driven insights about candidates. Integrated into Claude via MCP.
Repo: View on GitHub
Description: Analyzed insect antenna movement using DeepLabCut to extract behavior patterns from biological footage.
Technologies: DeepLabCut, Python, OpenCV
What I Did: Labeled keypoints, trained custom models, and analyzed movement trends.
Impact: Enabled high-accuracy behavioral studies in entomological experiments.
Description: Built a deep learning model to classify pollen images with over 90% accuracy.
Technologies: Python, TensorFlow, CNN, DenseNet121
What I Did: Created a labeled dataset, applied data augmentation, and trained CNN models.
Impact: Assisted agricultural researchers in automating pollen type recognition.
Description: A prediction engine for banking marketing campaigns using ML to forecast customer subscription behavior.
Technologies: Python, scikit-learn, XGBoost, Streamlit
What I Did: Applied feature engineering, handled class imbalance with SMOTE, and built a Streamlit interface.
Impact: Enabled accurate targeting for cost-effective campaign execution.
Description: Developed ML solutions in honey value chains for traceability and quality assurance.
Technologies: Python, TensorFlow, scikit-learn, REST APIs
What I Did: Collected field data, trained predictive models, and created RESTful endpoints.
Impact: Improved transparency across agri-food stakeholders and streamlined quality tracking.
Description: Identified academic performance factors using data analysis and ML.
Technologies: Python, Pandas, Scikit-learn, Tkinter
What I Did: Built a GUI tool that predicts student success based on multiple lifestyle and academic variables.
Impact: Provided actionable insights for educational policy and interventions.
2022 - Present: Specialization in Data Science and Machine Learning, working on advanced deep learning algorithms for classification tasks. Thesis focuses on using machine learning to automate processes in beekeeping and honey extraction.
2018 - 2023: Specialization in Data Science and Machine Learning, working on advanced deep learning algorithms for classification tasks. Thesis focuses on using machine learning to automate processes in beekeeping and honey extraction.
Email: ecedlplt9850@gmail.com
LinkedIn: Ece Dalpolat
GitHub: GitHub Profile
Medium: Medium Profile