Andranik Khachatryan, Machine Learning Developer in Yerevan, Armenia
Andranik Khachatryan

Machine Learning Developer in Yerevan, Armenia

Member since June 3, 2019
Ando has an AWS Machine Learning Specialty certificate, a Ph.D. in computer science, and is passionate about machine learning. He specializes in "classical" machine learning, as well as computer vision with deep learning, and is constantly fascinated with GANs. He has experience deploying multiple ML products with Amazon SageMaker.
Andranik is now available for hire


  • Envoy Media Group
    Amazon Web Services (AWS), Python, Amazon SageMaker...
  • Things Inc.
    Python 3, PyTorch, Jupyter, Jupyter Notebook, Google Colaboratory (Colab)...
  • Aisle3
    Python, Amazon Web Services (AWS), PyTorch, Deep Neural Networks...



Yerevan, Armenia



Preferred Environment

PyTorch, Python, Visual Studio Code

The most amazing...

...project has been creating an image search engine that would find the original from millions of candidates even if the query image was very heavily modified.


  • Machine Learning Lead

    2019 - PRESENT
    Envoy Media Group
    • Created an in-house framework that does auto-ML for data and tasks specific to Envoy Media Group. We used the AWS stack and could train and deploy a new model within an hour without writing code.
    • Contributed to feature our framework as a case study for Partner Success on AWS (
    • Trained, tested, deployed, and monitored machine learning models with Amazon SageMaker.
    • Collaborated with the Envoy Media Group team on their long-term AI/machine learning strategy.
    • Created a framework that supports easy model creation and lifecycle management, including monitoring and visualization. We have from 10s to 100s of live machine-learning models deployed in production.
    Technologies: Amazon Web Services (AWS), Python, Amazon SageMaker, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Gradient Boosted Trees, Data Science, Machine Learning, Docker, Jupyter Notebook, SciPy, XGBoost, Explainable Artificial Intelligence (XAI), Machine Learning Operations (MLOps), Natural Language Processing (NLP)
  • Machine Learning Engineer and Consultant

    2022 - 2022
    Things Inc.
    • Trained our own diffusion models using various approaches. These models were done on Google Colab notebooks using weaker GPUs and smaller datasets, but we managed to get things working and were ready for large-scale experiments.
    • Investigated a large number of papers and codebases related to Denoising Diffusion Probabilistic Models (DDPM) and Denoising Diffusion Implicit Models (DDIM).
    • Did research on competitors, such as Midjourney, to understand where the industry stands at the moment.
    Technologies: Python 3, PyTorch, Jupyter, Jupyter Notebook, Google Colaboratory (Colab), Computer Vision, Amazon Web Services (AWS), Amazon SageMaker, Generative Adversarial Networks (GANs), Diffusion Models, DDPM, DDIM, Image Processing
  • Head of Machine Learning

    2020 - 2021
    • Developed the product matching engine prototype, which matched the same product from different sellers using their images and description. Used OpenCV and ConvNet-generated image features, as well as vector index storage and search.
    • Oversaw the long-term ML strategy for the company, deciding which directions are the most promising going into the future. Worked closely with other teams on the overall system architecture on AWS.
    • Supervised a small remote team tasked with bringing the product matching engine into production. By the time I left the company, we had a product matching API and an internally-developed labeling tool using the API.
    Technologies: Python, Amazon Web Services (AWS), PyTorch, Deep Neural Networks, Artificial Neural Networks (ANN), Artificial Intelligence (AI), Computer Vision, OpenCV, TensorFlow, Deep Learning, FAISS, AWS Fargate, Amazon S3 (AWS S3), FastAPI, SQL, Image Processing, Jupyter Notebook
  • Amazon SageMaker Consultant

    2019 - 2020
    Visably LLC (via TopTal)
    • Provided consulting to the client to migrate their on-premise ML solution into Amazon SageMaker.
    Technologies: Amazon Web Services (AWS), Amazon SageMaker, Jupyter Notebook
  • Machine Learning Engineer (Remote)

    2019 - 2019
    Pirate Labs
    • Created a recommender system delivering marketing emails for a company with multiple, diverse clients. Each client is a shop selling different products, and the shops are very different from one another. We created a single system that works for all.
    • Designed, implemented, and tested the recommender system. It was highly configurable and flexible, enabling it to effectively adapt to unique settings of each shop.
    • Oversaw the testing and helped with launching the system into production.
    Technologies: Recommendation Systems, Factorization Machines, Python, Machine Learning, Data Science, NumPy, Pandas, SQL, Jupyter Notebook
  • Co-founder, CEO

    2018 - 2019
    NVision LLC
    • Created a cloud-based infrastructure for crawling, indexing, and supporting an image database of tens of millions of images.
    • Retrieved images from a database of tens of millions of images. Query images could be very heavily altered versions of the original.
    • Enabled digital watermarking of images (prototype).
    Technologies: PyTorch, TensorFlow, Keras, OpenCV, Python, C++, SQL, Image Processing, Jupyter Notebook, Generative Adversarial Networks (GANs)
  • Developer of Recommender System (Freelance)

    2017 - 2018
    • Helped prototype a recommender system.
    • Created, tested, and tuned a prototype with Python.
    • Implemented the system within AWS infrastructure and made it production-ready.
    Technologies: Amazon Web Services (AWS), Git, Jupyter, NumPy, Python, SQL, Jupyter Notebook
  • Product Manager

    2016 - 2017
    • Served as the product manager for an in-house developed ETL.
    • Participated in product creation from the start: design, implementation, testing.
    • Oversaw client deployments and service monitoring.
    Technologies: TFS, R, Visual Studio, .NET, C#
  • Head of Research and Education Center

    2012 - 2017
    • Created a highly demanded educational program with more than 10 applicants for one position.
    • Co-developed the overall strategy for the education center, including creating the curriculum, designing the admission process, and recruiting the lecturers. Managed 1-2 assistants who took care of day-to-day operations.
    • Managed entrance exams (up to three rounds) with more than 300 applicants and more than 20 lecturers/TAs/colleagues being involved in different rounds.
    • Researched and published in IEEE TKDE, currently ranked #1 by Google Scholar in the category "Databases and Information Systems."
    Technologies: R


  • Digital Watermarking with Deep Learning

    PyTorch implementation of paper "HiDDeN: Hiding Data With Deep Networks" by Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei:

  • Very Large Image Database with Advanced Search Functionality

    Image search database which indexes tens of millions of images and is able to find the original photo, even if query image is heavily cropped, resized, down-sampled, collage version of the original. Horizontally scalable, cloud-based.

  • Recommender System with Amazon SageMaker

    Made a recommender system for a startup as a remote member of the team. The final implementation utilized Amazon SageMaker to train and deploy the model.

    This was my first machine learning project, and it was fascinating. I started with a NumPy scratch-implementation and ended up using SageMaker, which had just been released at that time.

  • Machine Learning on Amazon SageMaker

    Worked on model training, testing, deployment, monitoring, and re-training on Amazon SageMaker.


  • Libraries/APIs

    PyTorch, NumPy, Keras, TensorFlow, SciPy, XGBoost, Scikit-learn, Pandas, OpenCV
  • Tools

    Amazon SageMaker, PyCharm, Jupyter, Git, Visual Studio, TFS, AWS Fargate
  • Paradigms

    Data Science
  • Platforms

    Amazon Web Services (AWS), Jupyter Notebook, Visual Studio Code, Docker
  • Other

    Game Theory, Deep Learning, Image Processing, Machine Learning, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Neural Networks, Deep Neural Networks, Computer Vision, Economics, Recommendation Systems, Factorization Machines, Clustering, Data Analysis, Generative Adversarial Networks (GANs), Computer Science, FAISS, FastAPI, Gradient Boosted Trees, Microsoft Azure, Google Colaboratory (Colab), Diffusion Models, DDPM, DDIM, Explainable Artificial Intelligence (XAI), Machine Learning Operations (MLOps), Natural Language Processing (NLP)
  • Languages

    Python, C#, SQL, C++, R, Python 3
  • Frameworks

  • Storage

    Amazon S3 (AWS S3), Google Cloud


  • Ph.D. in Computer Science
    2007 - 2012
    Karlsruhe Institute of Technology - Karlsruhe, Germany
  • Master of Science Degree in Computer Science
    2003 - 2005
    Yerevan State University - Yerevan, Armenia


  • AWS Certified Machine Learning - Specialty
    JANUARY 2020 - JANUARY 2023
    Amazon Web Services (AWS)
  • Sequence Models
  • Deep Learning Specialization
  • Convolutional Neural Networks
  • Neural Networks and Deep Learning
  • Structuring Machine Learning Projects
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
  • Machine Learning
  • Graph Analytics for Big Data
    JULY 2017 - PRESENT
  • Big Data Modeling and Management Systems
    JUNE 2017 - PRESENT
  • Machine Learning With Big Data
    JUNE 2017 - PRESENT
  • Big Data Integration and Processing
    JUNE 2017 - PRESENT

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