Aleksandar Milchevski, Machine Learning Developer in Skopje, Macedonia
Aleksandar Milchevski

Machine Learning Developer in Skopje, Macedonia

Member since October 24, 2019
Aleksandar has more than ten years of combined research and development experience working with data, machine learning, computer vision, and signal/image processing. He enjoys working remotely and solving complex problems. He prides himself on being able to write clean, readable code.
Aleksandar is now available for hire




Skopje, Macedonia



Preferred Environment

Amazon Web Services (AWS), Google Cloud, AWS, Python, Linux

The most amazing...

...thing I have worked on is a DNA sequencing software.


  • Machine Learning Engineer

    2020 - 2022
    Collab (Toptal Client)
    • Led the research and development of an API that does video processing.
    • Extracted valuable information from videos using AWS.
    • Used Ruby on Rails for the back-end side of the app.
    Technologies: Python, Amazon Web Services (AWS), AWS Rekognition, Computer Vision, Audio Signal Processing, Machine Learning, Data Science
  • Machine Learning Engineer

    2019 - 2021
    Miracle Mill, GmbH
    • Contributed to several projects using AWS (Glue, SageMaker, DynamoDB, and Lambda) and ETL using Apache Spark.
    • Led the choice, training, and validation of the machine learning models.
    • Placed the algorithms into production.
    Technologies: Amazon Web Services (AWS), AWS FireHose, AWS Glue, Amazon SageMaker, Machine Learning, AWS, Python
  • Audio Signal Processing Engineer

    2020 - 2020
    Leybold (via Toptal)
    • Analyzed, processed, and classified sound recordings.
    • Used discrete Fourier transform (DFT) and other signal processing techniques.
    • Used logistic regression and other machine learning techniques.
    Technologies: Machine Learning, Audio Single Proccessing, Digital Signal Processing, Python
  • Data Scientist

    2016 - 2019
    • Contributed to improving and developing production-ready software in C/C++.
    • Researched cutting edge ideas in the field of genomics and DNA sequencing.
    • Implemented and tested several complex ideas, including the training and testing of deep convolutional neural networks.
    Technologies: DNA Sequencing, Bioinformatics, Machine Learning, Keras, Python, R, C, C++
  • Computer Vision Engineer

    2016 - 2017
    Sentice Tech
    • Worked on anomaly detection in images.
    • Used OpenCV.
    Technologies: Python
  • Signal Processing Consultant

    2016 - 2016
    • Implemented the electrocardiogram (ECG) signal processing and denoising.
    • Implemented a pipelined discrete wavelet transform (DWT).
    • Researched the use of several FIR and IIR filters for the ECG denoting.
    Technologies: Digital Signal Processing, C++
  • Machine Learning Research Scientist

    2014 - 2016
    • Worked on research and development of a state-of-the-art emotion recognition algorithm.
    • Took part in the Fifth International Audio/Visual Emotion Challenge and Workshop.
    • Led the development of a people tracking solution through wifi. Implemented the model using Apache Spark.
    Technologies: Emotion Recognition, Machine Learning, MATLAB, Spark, C++, Python
  • Junior Teaching and Research Assistant

    2010 - 2014
    Faculty of Electrical Engineering and Information Technologies
    • Held auditory and laboratory exercises for several courses from the fields of digital signal processing.
    • Worked on power quality assessment. Used machine learning techniques to detect and classify the disturbances.
    • Took active participation in the research project "Algorithms for time-varying harmonic analysis for power quality assessment applicable on modern digital signal processors, ERA.NET PLUS project."
    • Utilized OpenCV to implement face detection using SVM.
    Technologies: Digital Signal Processing, Computer Vision, Machine Learning, MATLAB, C
  • Junior Researcher

    2009 - 2011
    Dip team
    • Worked on the detection and quantification of the intensity of ringing artifacts in JPEG coded images.
    • Developed a robust multi-frame super-resolution algorithm.
    • Got introduced to the fundamental ideas of machine learning and solving inverse problems.
    Technologies: Digital Signal Processing, Machine Learning, C, MATLAB


  • Multimodal Affective Analysis Combining Regularized Linear Regression and Boosted Regression Trees

    A multimodal approach for affective analysis was developed that exploits features from video, audio, electrocardiogram (ECG), and electrodermal activity (EDA) combining two regression techniques, namely boosted regression trees and linear regression. Moreover, a novel regularization approach for the linear regression was proposed in order to exploit the temporal correlation of the affective dimensions. The final prediction is obtained using a decision level fusion of the regressors individually trained on the different groups of features. The promising results obtained on the benchmark dataset show the efficacy and effectiveness of the proposed approach.

  • Improved Pipelined Wavelet Implementation for Filtering Ecg Signals

    In this project Discrete Wavelet Transformation implementation of a bandpass filter for denoising an ECG signal was made. New improved version of a DWT bandpass filter was realized using a circular buffer. Time performance analysis and comparison of obtained solution vs. existing solutions was done.

  • Machine Learning Based Super-Resolution Algorithm Robust to Registration Errors

    In this work, a novel two-phase approach is proposed for robust super-resolution in the presence of registration errors and outliers. In the first phase, a machine learning method is used to create a weight matrix for every LR (low resolution) image indicating the presence of registration errors. In the second phase, super-resolution is performed using all of the LR images and the associated weight matrices, creating an image that is free of error artifacts.

  • Generic Face Detection and Pose Estimation Algorithm Suitable for The Face De-Identification Problem

    In this work, the problem of face de-identification in an image was tackled. The first step towards a solution to this problem is the design of a successful generic face detection algorithm, which will detect all of the faces in the image or video, regardless of the pose. If the face detection algorithm fails to detect even one face, the effect of the de-identification algorithm could be neutralized. That is why a novel face detection algorithm was proposed for face detection and pose estimation. The algorithm uses an ensemble of three linear SVM classifiers. The first, second, and third SVM classifier estimates the pitch, yaw, and roll angle of the face and logistic regression is used to combine the results and output a final decision. Second, the results of face detection and a simple space-variant de-identification algorithm are used to show the benefits of simultaneous face detection and face de-identification.


  • Paradigms

    Data Science
  • Other

    Machine Learning, Digital Signal Processing, Big Data, Deep Learning, AWS FireHose, Audio Single Proccessing, DNA Sequencing, Computer Vision, Emotion Recognition, Signal Processing, Electronics, Audio Signal Processing
  • Languages

    C++, C, Python, R
  • Tools

    MATLAB, AWS Glue, Amazon SageMaker, AWS Rekognition
  • Storage

    Google Cloud
  • Frameworks

  • Libraries/APIs

    TensorFlow, Keras
  • Platforms

    Linux, Amazon Web Services (AWS)
  • Industry Expertise



  • Progress towards a Ph.D. in Machine Learning
    2013 - 2015
    Faculty of Electrical Engineering and Information Technologies - Skopje, Macedonia
  • Master's Degree in Signal Processing
    2009 - 2013
    Faculty of Electrical Engineering and Information Technologies - Skopje, Macedonia
  • Bachelor's Degree in Electronics and Signal Processing
    2005 - 2009
    Faculty of Electrical Engineering and Information Technologies - Skopje, Macedonia


  • AWS Certified Data Analytics - Speciality (prev. Big Data)
    JULY 2020 - JULY 2023
  • AWS Machine Learning - Speciality
    NOVEMBER 2019 - NOVEMBER 2022
  • Google Cloud Certified Professional Data Engineer
    Google Cloud
  • Data Engineering, Big data and Machine Learning on Google Cloud Platform
    APRIL 2019 - PRESENT
    Coursera (GCP)
  • Machine Learning with TensorFlow on Google Cloud Platform
    Coursera (GCP)
  • Deep Learning
    JUNE 2018 - PRESENT
    Coursera (

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