About
I am a Machine Learning Engineer specializing in computer vision and deep learning, with hands-on experience taking models from research to production. Over the past years, I've led and contributed to AI-driven projects across medical imaging, industrial object tracking, and behavior-informed modeling for EV charging systems. I am currently pursuing an MSc in Data Science at Gazi University.
I led the Medcom AI team focused on healthcare, where we developed deep learning models and became finalists in the Teknofest Artificial Intelligence in Health Competition. I also mentored aspiring AI professionals through nonprofit initiatives, helping them gain essential skills in the field.
My passion lies in applying AI to solve real-world problems—whether through data analysis, predictive modeling, or developing innovative solutions in R&D. I am continuously expanding my skills in deep learning, transfer learning, and scalable AI systems, and I am eager to contribute to the continued growth and transformative potential of artificial intelligence.
Most good programmers do programming not because they expect to get paid or get adulation by the public, but because it is fun to program.
-Linus Torvalds
Do not let your fire go out, spark by irreplaceable spark in the hopeless swamps of the not-quite, the not-yet, and the not-at-all. Do not let the hero in your soul perish in lonely frustration for the life you deserved and have never been able to reach. The world you desire can be won. It exists.. it is real.. it is possible.. it's yours.
-Ayn Rand
Skills
Technical
- Python: pandas, numpy, opencv, tensorflow, pytorch, keras
- Machine Learning & Deep Learning: CNN, RNN, LSTM, Transformers, Transfer Learning, Behavioral Prediction
- Computer Vision: object detection, object tracking, medical imaging, MMDetection, MMTracking, YOLO, Faster R-CNN, ResNet, FPN, DeepSort, ReID
- Data Analysis & Visualization: SQL, MSSQL, data dashboards
- Mobile & Web Development: Flutter (iOS/Android), C#, .NET Core, MVC architecture
- Dev Tools: Git, Google Colab, Jupyter Notebook
Professional
- Analytical Thinking
- Problem-Solving
- Team Worker
- Strong Communication
- Innovative
- Leadership
- Project Management
CV
Summary
Salih Enes Metin
Machine Learning Engineer specializing in computer vision and deep learning, with hands-on experience taking models from research to production. Delivered AI solutions across medical imaging (Teknofest AI in Health finalist), industrial object tracking, and behavior-informed modeling for EV charging systems. Proficient in PyTorch, TensorFlow, and OpenCV, with a proven ability to integrate deep learning models into cross-platform products. Currently pursuing an MSc in Data Science at Gazi University.
- Ankara, Türkiye
- metin.salihenes@gmail.com
Education
Master's Degree in Data Science
2026 - Present
Gazi University, Ankara, Türkiye
Relevant coursework: Predictive Modeling in Data Science, Advanced Data Mining, Machine Learning, Time Series Analysis.
Bachelor's Degree in Computer Engineering
2019 - 2024
Cukurova University, Adana, Türkiye
While studying at university, I joined several clubs, including the Community Volunteers Club, where I volunteered to help others, and engineering and team collaboration-focused clubs like IEEE. Additionally, I took on the role of Project Team Lead for a community established at school under the Huawei Student Developers program. For this community, I created a software entry training program for around 40-45 students and later helped them form teams to participate in competitions or projects. Moreover, I formed a team with two friends called Medcom AI. The team's goals were to develop original projects, pioneer works in artificial intelligence, focus on health applications in AI, and participate in competitions. With this team, we advanced to the final round of a Teknofest competition and completed numerous projects.
Experience
AI Software Engineer & R&D Engineer
Renpro, Ankara, Türkiye
Jan 2025 - May 2026
Designed and implemented AI-based solutions to improve the efficiency of electric vehicle (EV) charging station management systems. Developed behavior-informed machine learning models to support operational decision-making, training and fine-tuning deep learning models in PyTorch to adapt to changing user patterns. Built cross-platform mobile applications with Flutter (iOS and Android) delivering model-driven features to end users in real time, and contributed to Python backend services within a cross-functional R&D team.
Machine Learning Engineer Intern
Teknology House, Adana, Türkiye
May 2024 - Jun 2024
Developed an object tracking and counting system for a production line using the DeepSort algorithm; trained ReID and detection models in PyTorch and unified them into a single inference pipeline. Created and annotated a custom dataset for object tracking and feature extraction tasks.
Software Developer Intern
Arçelik, Istanbul, Türkiye
Jul 2023 - Sep 2023
Integrated an advanced data analysis dashboard into the AGV (Automated Guided Vehicle) Management System using C#, .NET Core, and Python.
Certificates
Deep Learning Specialization
Coursera
TensorFlow Developer Certificate
TensorFlow
Data Analysis
Global AI Hub
Events
Start Up Weekend for all
2019
UNDP
In this event, held under the United Nations Development Programme, I had 48 hours to come up with an idea and form my own team. Afterwards, I developed this idea with my team. I discussed this idea with mentors and eventually made a presentation to impress the jury and gain support. It was a very useful and enjoyable experience for me.
METU VTOL Competition
2022
BOEING
As the SEAGUL team, we successfully passed the conceptual design report of the first stage in the METU VTOL 2022 competition, which was held for the sixth time under the sponsorship of Boeing, 27 teams from 15 different universities from 3 different countries were accepted to the competition and moved on to the next stage.
Artificial Intelligence in Health Competition
2022
Teknofest
As the leader of the Medcom AI team, I led our project to become a finalist in the Teknofest Health Artificial Intelligence Competition, where we developed a system to detect seven different diseases in abdominal tomography images.
Projects
Computer Vision-based Disease Detection for the Abdominal Region
An application has been developed using CT series of the abdomen, both with and without contrast, for seven different clinical conditions that resulted in emergency department admissions with complaints of abdominal pain. These data were used to train an original model combining YOLO and ResNet for the desired classification predictions. Finalist in the Teknofest AI in Health Competition (2022). Read More
BIRADS Category and Breast Composition Prediction in Screening Mammography with Computer Vision
In this project, a ResNet-based model was fine-tuned on TÜSEB's screening mammography data to perform BIRADS category and breast composition classification. Read More
Cukurova University CENG Q&A BOT
In this project, a question answering bot based on the word2vec model trained with the QA data set was developed for the computer engineering department of Çukurova University. The data set was divided into categories and a separate model was developed for each category.Read More
Product Line Object Tracking with mmtrack
This project focuses on the application of artificial intelligence for object tracking and counting on a product line using the MMTracking library. The project addresses challenges such as accurate object detection and tracking in a dynamic production environment. Read More
UK Sponsor List - Tier 2 Visa Sponsorship Search Platform
A Web, Android, and iOS platform helping users find licensed Tier 2 visa sponsors in the UK, featuring advanced filtering, search, and a favorites list. Live on the App Store and Google Play. Read More
My Blog Posts
Contact
Email:
metin.salihenes@gmail.com