Upon joining the company, a simultaneous goal was initiated to launch the Modelic web application in 2022. The objective of this application is to establish a B2B platform that offers diverse BIM models to customers on both small and large scales. Through this platform, customers, company administrators, and engineers can collectively navigate all project stages—ranging from initial consultations, pricing, and project execution by the company's architect engineers, to monitoring project progress and facilitating payments—efficiently and collaboratively. The integration of artificial intelligence in segmenting architectural models and pointclouds further accelerates project progress and reduces architects workload.In this project, my primary responsibility involves constructing the backend utilizing the FastAPI framework. My duties throughout this project encompass:● Led a strategic migration from Python-based (FastAPI) backend services to Golang microservices, resulting in a 30% improvement in response times● Developing the backend using the FastAPI framework in conjunction with a MongoDB database.● Setting up the project's initial CI/CD and DevOps infrastructure utilizing NGINX, Docker, Docker Compose, and GitHub actions.● Developing an object storage system using Minio on server. This system facilitates the uploading and downloading of architectural data by both clients and architects.● Collaborated closely with multidisciplinary teams including the Front-End team proficient in Next.js and React, DevOps team, and Forge Viewer Developers team.● Crafting user stories and effectively managing the product backlog.● Maintaining close collaboration with developers and overseeing task management.● Collaborating with stakeholders on app goals and user interface design, while taking into financial and time constraints.● Developing a segmentation model using TensorFlow's Keras library and the WUNET architecture. (At R&D phase)

Participated in an intensive Golang-centric bootcamp, covering foundational to advanced topics and culminating in multiple projects showcasing microservice architectures and real-world backend scenarios. Key topics and tools included:• Core Golang Concepts: Variables, Types, Control Structures, Arrays, Functions, Pointers, Structs, Interfaces• Advanced Patterns & Tooling: Concurrency, Testing, Package Management, Design Patterns• Database & Linux Basics: SQL operations, Linux fundamentals• Networking & Web Protocols: Understanding the OSI model, Network Basics, TCP/HTTP protocols, Client-Server architecture, Tools like Curl and Postman• Golang Web Development: net/http package, HTML/Templates, Fiber framework (routing, middleware), Context usage, ORM integration, API design with JSON and serialization• Microservices & Architecture: Principles of RESTful APIs, Authentication, Queues (RabbitMQ, NATS), Docker for containerization, Project architecture patterns (DDD, hexagonal structure), gRPC, Protobuf for service communication• Scaling & Observability: Scaling Golang applications, Log management, Monitoring, Caching strategies (Redis), Asynchronous programming• Completed three major projects integrating NATS, RabbitMQ, gRPC, DDD design, Protobuf, Redis caching, and logging, along withnumerous smaller exercises to reinforce these concepts

I submitted a proposal for military service and was accepted into the "elite military" plan. During that period, I was assigned two projects:1. Production of Land Cover Map using Sentinel 2 Satellite Images and Google Earth Engine Processing Platform.The primary objective of this project was to create a precise land cover map. This involved applying advanced machine learning techniques such as supervised and unsupervised methods (k-means, G-means, artificial neural networks, support vector machines, random forest, multi-label classification), along with utilizing Sentinel 2 satellite data, digital elevation models, and additional data sources. Furthermore, this project entailed precise result analysis and validation.2. Construction of a Highly Accurate and Efficient CNN Model for Building Change Detection.For this project, my goals included:● Developing a robust building detection model using UNET segmentation models with a targeted accuracy of over 90%.● Developing a dependable model for change detection in buildings, maintaining an accuracy level of above 90%.● Generating a DSM (Digital Surface Model) map by utilizing stereo images acquired from an aircraft, and subsequently employing it as supplementary data for model training.● Identifying the optimal parameters for effective model training.● Thoroughly examining the results to improve data quality and model performance, including finding any issues with the available data and labels.● Performing preprocessing on the primary images to rectify any existing data imperfections.

During my academic journey, I developed a profound interest in artificial intelligence, neural networks, and image processing. My fascination extended to leveraging practical and extensive datasets within the industry, primarily utilizing Python.From my very first year, I embarked on simultaneous efforts with esteemed university professors to craft two articles. These collaborative endeavors culminated in successful publications. Consequently, I was approached to share my knowledge through providing lectures and guidance to fellow students in the computer department. Some of the topics I had the privilege of teaching and advising on included:● Introduction to Python● Essential Python libraries: numpy, pandas, matplotlib, opencv, keras, and more● Working with satellite and aerial images, encompassing the analysis of distinctive features for each type● Machine learning algorithms and the utilization of libraries such as scikit-learn● Training and testing neural networks while rigorously scrutinizing the ensuing outcomes● In-depth exploration of Convolutional Neural Networks (CNNs)● Comprehensive guidance on composing and submitting articles to be included in ISI-indexed journals

My responsibilities encompassed utilizing AutoCAD Civil 3D software for precise drawings and updating the map of Tehran.