Alexander Lichtenstein

Developed a platform for the client from the real estate industry, including the back end, admin panel, and app. The main purpose of this app was to connect tenants and landlords, as well as to give a price estimation for real estate and make forecasts about the price development in the following years. Also, the platform was able to perform automatic data extraction from tenant contracts and utility bills (as photos) and add this information to the database on the fly.

RESPONSIBILITIES
• Regression model for price estimation and forecasting
• NLP model for data extraction from the documents
• Programming of the front-end part (Ionic app)
• Programming of the back end
• Testing and deployment of the project

Developed a mobile app for disability card verification. This app's primary function is cross-checking a person's face and disability cards. After successful verification, a personal ID code was generated.

The main technical issue in this app is comparing selfies and disability card photos due to the different resolutions and quality. The problem was solved with the help of deep learning.

RESPONSIBILITIES
• Team lead.
• Programming of microservices and deployment on the cloud (Flask)
• Implementation of the deep learning model for the comparison of photos
• Testing and deployment of the project

Development of the mobile app for a big corporate client. The main purpose of this app is to provide content on health topics to the end users. Our implementation was based on Firebase (Google Cloud). In addition, we have integrated the customer database to provide access to existing clients.
The content of the app is controllable through the admin panel.

Responsibilities:
• Project architecture.
• Team lead.
• Programming microservices and deploying on the cloud.
• Implementation of the recommendation system (based on articles tags, ML solution) for the end user.
-Testing and deployment of the project.

The technology stack consisted of Firebase, Flutter, and GCP.

Development of the advertising platform that connects streamers and advertisers. With the help of this solution, advertisers can turn on their
ads in the streams and streamers get paid.

Responsibilities:
• Project architecture.
• Team management.
• Testing and deployment of the project.

The technology stack included React, Java Spring, and GCP.

I developed a mixed fuzzy logic and NLP solution for matching product descriptions in the company's and its customers' databases. For this purpose, I implemented a data cleaning procedure and created a fuzzy logic matching procedure based on various metrics, such as Levenshtein distance and cosine modularity. In addition, I integrated pre-trained Bert models into the process. I was able to achieve high matching performance on the clients data.

• Leveraged the power of GPT (like ChatGPT) to design a chatbot proficient in navigating through structured and unstructured company data, ensuring real-time responses to complex queries.
• Developed mechanisms to autonomously collect data from corporate sources, ensuring the chatbot is always up-to-date with the latest company information, solving the indexing problem.
• Set up advanced ML pipelines to process, validate, and integrate data from various sources, ensuring the chatbot's responses are consistent and reliable.
• Played a key role in co-developing the overall platform architecture, from designing an optimal database structure capable of handling a plethora of corporate data to creating an efficient API for potential integrations.