src/cv/me.txt

Alexander Todorov 
Email: alex.st.todorov@gmail.com

LinkIn https://www.linkedin.com/in/alexander-t-50864a139/

Summary: 
Software and ML Engineer & Data Scientist with over 5 years in software development, building AI/ML solutions, LLM tools, recommendation systems, and data-driven web applications using Python, Flask, Angular, Spark, and MongoDB. Skilled in microservices, Docker, Kubernetes, CI/CD, NLP, and Azure OpenAI to deliver scalable and automated enterprise solutions. Passionate about creating intelligent, user-centric software that drives innovation and operational efficiency.



Experience: 
2020 - Present - Intel Corporation
Data scientist and Ml Engineer 

Led the development of an enterprise-level AI recommendation platform that analyzes employee skills and activity to recommend internal training courses.

Designed and deployed scalable ML pipelines for recommendation systems, predictive maintenance, and NLP topic modeling, using Python, Spark ALS, Logistic Regression, BERTopic and Azure OpenAI GPT-4.


Built cloud-native microservices for LLM-driven using Kafka, Kubernetes, Docker, and MongoDB


Engineered automated  CI/CD workflows with GitHub Actions


Automated analytics flows and BI data pipelines using containerized Python jobs and Kubernetes CronJobs, supporting real-time and scheduled reporting.


Applied advanced NLP and LLM techniques to classify topics, extract meaningful patterns from ticket history, and automate text understanding at scale.


2012 - 2020   - Intel Corporation 
Process Eng - Production process analyses
Python scripts, SQL , Automated reports development, Angular
Intel Ireland 2013 - 2017 - Supporting the startup of new technology. Process and equipment 

Education:

2025 - Today - Technical University – Sofia,Bulgaria.
PhD Program - Robotic and automation systems with AI 

2020–2024 - Technical University – Sofia,Bulgaria.
Master’s degree in Robotic Engineering. 
⋄ Final project: Robot ARM design with Python ROS framework

2018-2019 - John Bryce Collage 
Full Stack - Angular, Java, SQL 

2009–2012 - Singalovsky College Tel-Aviv. 
Electronic Practical Engineer. 



Skills: 
AI: PySpark, Spark ALS, Logistic Regression, BERTopic, Azure OpenAI GPT-4, NLP, LLMs, Recommender Systems, Topic Modeling
DevOps: Docker, Kubernetes, Cloud Foundry, GitHub Actions, Kafka, CI/CD, Microservices
Programming: Python, Flask, FastAPI, Angular, MongoDB, SQL
DB : Mongo DB, SQL , PostGres 


Languages
Hebrew – Fluent
English – Advanced; used daily in an international corporate environment
Bulgarian – Native
Russian – Native

Hobbies: 
Swimming , drones, snowboarding 



Projects: 

AI Recommendation System
Led the development of an enterprise-level AI recommendation platform that analyzes employee skills and activity to recommend internal training courses.
 Technologies: Python, Angular 18, MongoDB, Spark ALS, Cloud Foundry, Azure SSO, NLP, Docker
Designed and managed the end-to-end system architecture across backend and frontend components.
Implemented a recommendation engine using ALS on Apache Spark.
Integrated Azure SSO for secure user identification and behavior tracking.
Automated personalized email notifications based on work schedules and recommendation results.
Deployed the solution on an on-premise Cloud Foundry environment.



ML Preventive Maintenance System
Developed a machine-learning model to predict maintenance needs for manufacturing equipment using historical operational data.
Technologies: Python, Logistic Regression, Pandas, Scikit-learn
Built a predictive analytics pipeline to estimate the next maintenance cycle.
Enabled early warning for failures and improved production reliability.



BI Automated Reporting & Workflow Orchestration
Built automated BI data flows and reporting pipelines based on production and operational data.
 Technologies: Python, Docker, Kubernetes (Rancher), CronJobs, Angular, Power BI
Created containerized Python services to compute metrics and generate reports.
Deployed workloads as Kubernetes Deployments and CronJobs on Rancher.
Provided dashboards in Angular or Power BI for management visibility.


NLP / ML Ticket Topic Modeling
Implemented NLP-based topic modeling to detect recurring issues in ticket history.
Technologies: BERTopic, Python, NLP, Scikit-learn
Automated detection of common problem categories to support root-cause analysis.
Delivered insights that improved support efficiency and reduced repeated issues.



LLM Microservices for PDF Data Extraction
Designed a distributed LLM system to extract structured data from large PDF documents.
Technologies: Python, PyPDF, Azure OpenAI GPT-4, Kafka, Kubernetes, Microservices, MongoDB
Built a microservices architecture for document scanning, chunking, and LLM interaction.
Used Azure OpenAI GPT-4 for high-accuracy extraction and summarization.
Implemented asynchronous communication through Kafka.
Presented at the internal AI Summit, gaining organizational recognition.


CI/CD for Web Applications (GitHub Actions)
Developed automated CI/CD pipelines for Angular-based applications.
Technologies: GitHub Actions, Angular, Docker, Kubernetes
Automated build steps: Angular build → Docker build → Push → Kubernetes deployment.
Enabled rapid and reliable releases for production systems.

Full-Stack Podcast Web Application
Built a full end-to-end platform for uploading, managing, and playing audio content.
Technologies: Angular, Python Flask, MongoDB, Docker
Implemented user interface for playback and an admin panel for file management.
Added support for metadata, thumbnails, and user comments.
Designed a scalable backend with REST APIs and MongoDB.



Issues Ticketing System (ServiceNow + Custom Frontend)
Developed a complete ticket management system integrated with ServiceNow.
Technologies: JavaScript, Angular, ServiceNow, Kubernetes, Azure SSO
Created dynamic ticket forms, routing logic, and an Angular-based landing page.
Integrated Azure SSO for authentication and user tracking.
Implemented automated reporting on ticket types, frequency, and recurring issues.