Martin Yanev

I enjoy building software for large-scale distributed AI systems.

mpyanev@gmail.com
2022 –
Waters Corp
Joined Waters Corporation as a Software Engineer.

I develop firmware and backend solutions for advanced medical instrumentation, including embedded C/C++ systems, Python-based AI agents, automation tools, and graphical firmware loaders. My work focuses on improving accuracy, throughput, and developer productivity in HPLC sample management and related technologies.

• Meanwhile in 2024, I authored the book Building AI Applications with OpenAI APIs, published by Packt Publishing.
View on Google Books
2023 –
Fitchburg State University
I was invited by the Head of the Computer Science Department, Brady Chen, to join Fitchburg State University as an Adjunct Professor.I teach courses focused on practical AI development and core CS topics:

• CSC7255: Data Communications & Networking
• CSC7132: Operating Systems
• CSC8028: Building AI Applications with OpenAI APIs
• CSC8025: Computer & Network Security
2021 – 2022
IVY Technology
I worked at IVY Technology as a Software Engineer. I developed Python tools and supported network device management and debugging for Cisco product families including C9200, C9300, Nexus, and ASR A900.
2020 – 2021
Fitchburg State University
Due to my passion for software engineering and AI, I moved to the United States and earned my M.S. in Computer Science from Fitchburg State University.
2018 – 2021
Indra Systems
I worked at Indra Systems in the United Kingdom as a Software Engineer. I developed, integrated, and tested components for next-gen European air traffic management system iTEC, executed over 1,000 tests, and contributed to achieving a 95% pass rate on Functional Acceptance Testing with the client.
2018
Cranfield University
Moved to United Kingdom to specialize in Aerospace Eng, earned M.S. from Cranfield University.
2017
Technical University of Sofia
Earned my B.S. in Aeronautical Engineering from Technical University of Sofia.
bio
Martin Yanev is a skilled software engineer with nearly a decade of experience in AI, embedded systems, networking, and full-cycle software development. He specializes in Python and C++, with deep expertise in building AI/LLM applications & agents, automation frameworks, CI/CD pipelines, and firmware. He is the author of Building AI Applications with OpenAI APIs and an adjunct computer science professor who has created over 30 courses and maintains 100+ open-source projects on GitHub.


teaching
I have a YouTube channel where I share lectures and content on computer science and AI.
In 2023 I designed and became the primary instructor for a groundbreaking AI applications course at Fitchburg State University ❤️. The course was built around my book, Building AI Applications with OpenAI APIs , and integrated cutting-edge AI technologies. Since its launch, the class has quickly grown in popularity, attracting students eager to gain hands-on experience with AI-driven application development.

publications & media
Selected media features on my journey in AI, software, and education:
git projects
This list showcases some of my favorite personal and open-source projects. Check out my GitHub for more!

Practice Test App is a Django-based web application for generating and managing practice tests powered by OpenAI APIs. Features include Stripe integration for payments and deployment on Azure Cloud.
AI Applications — A collection of 10 practical AI-powered applications built in Python using Flask, Django, Pandas, and other libraries. All projects are deployed on Azure with monetization and subscription features integrated.
Book To Course AI Agent — An agentic Python system that transforms static textbooks into structured, long-form educational videos. It uses AI assistants (including GPT-4o), DALL·E, Stable Diffusion for script generation and visuals, book segmentation, code extraction, and MoviePy for assembling tutorials with slides, illustrations, and live coding demos. See the related publication.
Masked Self-Attention — A from-scratch Python implementation of the masked self-attention mechanism central to autoregressive Transformers. It demonstrates core concepts like Q/K/V matrices, scaled dot-product attention, softmax, and causal masking for sequence modeling.
Schema-Validated Gemini & OpenAI API Responses — A lightweight Python library (publicly available) that enforces structured, schema-validated outputs from OpenAI and Gemini APIs. It includes type-safe JSON parsing, prompt augmentation, error handling, and multimodal support (using HTTPX and Pillow). Improves reliability for production AI integrations. See the related publication.