Lazar Stamenkovic, Developer in Niš, Serbia
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Lazar Stamenkovic

Verified Expert  in Engineering

Machine Learning Developer

Niš, Serbia
Toptal Member Since
August 11, 2021

Lazar is a machine learning engineer with four years of professional experience. He is enthusiastic about building AI-based solutions, from development to deployment. He's acquired strong skills in coding and algorithms by participating in various programming competitions and working in the industry. Lazar has experience working in both companies' and universities' research teams, and he always endeavors to contribute with his knowledge and ideas.


United Cloud
Machine Learning, Computer Vision, Image Recognition, Video Processing...
Zucchabar Inc.
Artificial Intelligence (AI), Generative Pre-trained Transformers (GPT)...
Zucchabar Inc.
Algorithms, Natural Language Processing (NLP)...




Preferred Environment

Linux, PyCharm, Slack, Skype, GitHub

The most amazing...

...project I've proposed and developed is a question-answering system that had better performance than the company's previous approach.

Work Experience

Machine Learning Engineer

2020 - 2022
United Cloud
  • Collected, analyzed, and generated large datasets for image and video processing tasks like channel logo detection and fingerprint recognition.
  • Developed deep learning solutions for the mentioned tasks that speed up the entire system more than 100 times versus previous company approaches.
  • Collaborated on a project for human detection and segmentation.
  • Served production-ready solutions using a FastAPI Python framework, Apache Kafka, the Tensor RT deep learning inference engine, and Docker.
  • Cooperated on semantic video segmentation and event localization, from research to development.
Technologies: Machine Learning, Computer Vision, Image Recognition, Video Processing, TensorFlow, PyTorch, Keras, FastAPI, Apache Kafka, Docker, Deep Learning, Image Processing, APIs, Convolutional Neural Networks (CNN), Deep Neural Networks, Artificial Intelligence (AI), Image Analysis, Neural Networks

Machine Learning Engineer

2019 - 2021
Zucchabar Inc.
  • Developed and proposed a new approach for question-answering that outperformed the company's previous one.
  • Collected the data and developed a model that recommended study groups for users based on their biography and interests.
  • Worked with other team members on the system for students assessment, including questions' generation from the completed course.
Technologies: Artificial Intelligence (AI), Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), TensorFlow, Keras, Flask, MongoDB, Git, Docker, Vue, APIs, FastAPI, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNN), Transformers, Text Embedding, Machine Learning, Amazon Web Services (AWS), Amazon S3 (AWS S3), Neural Networks, Recommendation Systems

Junior Machine Learning Engineer

2018 - 2019
Zucchabar Inc.
  • Developed an algorithm for text classification which assigns the tree of topics to a given text.
  • Created a project template for deploying machine learning models using Flask and Flask-RESTPlus. The entire deploying flow was unified and speeded up.
  • Built the annotation and testing tool using Flask, Vue.js, and MongoDB.
Technologies: Algorithms, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Python, Python 3, Artificial Intelligence (AI), Machine Learning, Text Classification, Flask, Flask-RESTful, Pandas, Neural Networks, Deep Neural Networks

Junior Software Engineer

2017 - 2018
  • Implemented CRUD API endpoints using Flask and SQLAlchemy.
  • Created new UI components, integrated the Vuex store, and implemented API calls in Vue.js.
  • Scraped web resources using Python programming language and libraries such as Scrapy and Selenium.
Technologies: Vue, Flask, Django, SQL


Codiss.Ai is a web application and chrome extension for saving and semantic searching code snippets. The application consists of the following parts:
• A RESTful FastAPI application.
• A UI Vue.js application.
• A search engine implemented by a vectors' similarity search and text embedding DL model.
• A Chrome extension that enables a more effortless saving of code snippets.

Codiss.Ai is a personal project.

Optimization of a Health-service Schedule

This project aimed to optimize a health service schedule by predicting patients' cancellations and absences. The prediction is based on a model trained with actual data collected in several health institutions in Serbia from 2010 to 2019.

The research paper presented at the ICIST 2020—10th International Conference on Information Society and Technology— at Kopaonik, Serbia, to which I've contributed, is available in the link above.

Herbs Classification

Herbs Classification is a Windows desktop application for classifying images of herbs and getting information about them. The dataset was collected manually and with scraping. Convolutional neural networks were used, such as VGG, ResNet, and Inception Net.

Herbs Classification is a faculty project of mine.
2018 - 2020

Master's Degree in Software Engineering

Faculty of Sciences and Mathematics - Nis, Serbia

2015 - 2018

Bachelor's Degree in Computer Science

Faculty of Sciences and Mathematics - Nis, Serbia


Build a Back-end REST API with Python and Django – Advanced



TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Vue, SpaCy, Vuex, Flask-RESTful, PyTorch, OpenCV, Matplotlib, REST APIs


Git, Jupyter, PyCharm, Slack, Skype, GitHub, Named-entity Recognition (NER), Docker Compose


Python, Python 3, C++, JavaScript, SQL, Java, R, C#


Flask, Vuetify, Django, Selenium, Windows Presentation Foundation (WPF)


Linux, Docker, Apache Kafka, Amazon Web Services (AWS)


MySQL, MongoDB, PostgreSQL, Amazon S3 (AWS S3)


Artificial Intelligence (AI), Machine Learning, Deep Learning, Natural Language Processing (NLP), Algorithms, Text Embedding, Logistic Regression, Convolutional Neural Networks (CNN), Deep Neural Networks, Recurrent Neural Networks (RNNs), Transformers, Neural Networks, Generative Pre-trained Transformers (GPT), Computer Vision, FastAPI, Image Recognition, Video Processing, Natural Language Understanding (NLU), Natural Language Generation (NLG), Search Engines, Image Processing, APIs, Web Scraping, Data Visualization, Supervised Learning, Unsupervised Learning, Text Classification, Sentiment Analysis, Image Analysis, Recommendation Systems, Web Crawlers

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