Python Developer

Der Kandidat ist ein Computer Vision Engineer, der sich hauptsächlich für KI interessiert. Was seine Berufserfahrung angeht, so arbeitet er seit fast 2 Jahren mit Python. Allerdings hat er bereits während seines gesamten Studiums mit Python gearbeitet. In seinem derzeitigen Job ist er für die Entwicklung von Modellen verantwortlich, die auf visuellen Daten beruhen. Außerdem verarbeitet er die von der Kamera kommenden Bilder auf der Grundlage der vom Kunden benötigten Merkmale. Zum Beispiel schreiben sie für Blinde und Sehbehinderte, damit sie lesen können. Darüber hinaus beschäftigt er sich mit anderen KI-Themen wie Objektiverkennung, Texterkennung, Erkennung, Optimierung und vielem mehr. Neben Python verwendet er auch Bibliotheken wie Scikit, Tensorflow, PyTorch und Keras. Django verwendet er manchmal zu Demonstrationszwecken, aber er hat ein Jahr kommerzielle Erfahrung. Was AWS betrifft, so hat er jetzt 2 Jahre Erfahrung. Außerdem hat er schon immer mit agilen Methoden wie Scrum gearbeitet. Es macht ihm auch Spaß, ein Team zu leiten, daher ist er offen dafür, in Zukunft Führungsaufgaben zu übernehmen.

• Dostupnosť Mitte Juli/August

Computer Vision Engineer (Dates: Unknown)

• Led a project to develop a real-time document guidance feature for the blind and visually impaired using image segmentation.
• Managed data collection and annotation pipelines on a large scale.
• Customized the model architecture to increase on-device inference speed by 1000%.
• Implemented a mask consistency algorithm and customized loss function to reduce False Positives by 14%.
• Re-implemented a text detection model from PyTorch to Tensorflow 2.x for on-device detection of incidental text, resulting in a 400% increase in inference speed.
• Applied weight pruning to reduce model size by 20%.
• Implemented face detection and alignment using BlazeFace for Mobile GPU inference on Google Glasses.
• Implemented face recognition module based on MobileNetv3 feature extraction and Cosine Similarity.
• Implemented a text orientation classifier for a better reading experience for the visually impaired and blind.
• Implemented real-time 1D barcode detection using traditional computer vision methods.

Principle AI Engineer (Dates: Unknown)

• Managed a team of 3 to implement data in a graph database platform (neo4j).
• Designed data models for a large amount of unstructured data and exported them to a graph database.
• Managed a team to design a tool for synthetic data generation.

Computer Vision Engineer (Dates: Unknown)

• Implemented a text localization Neural Network to improve the accuracy by 5% (mAP) compared to the previous solution.
• Implemented and benchmarked a language agnostic text recognition network.
• Designed and implemented an API and web platform for a complete text detection platform.
• Implemented an API with Django for measuring image similarity based on MobilenetV2 features and cosine similarity metric.
• Benchmarked a large-scale image dataset on GCP cloud vision and AWS Rekognition to improve production accuracy.
• Improved the speed of API calls by 50% for the benchmark by using Asynchronous Python with AsyncI/O.

Backend Developer Intern (Dates: Unknown)

• Developed a web API based on Django-Rest framework.
• Debugged data synchronization between the web platform and the Android app.

Publication Record:

• Accepted paper "AI-assisted image analysis of cervical spine motion X-Ray images" at EuroSpine 2022.
• Published paper "t-EVA: Time-Efficient t-SNE Video Annotation" at ICPR 2020.

Education:

• Master of Embedded Software Engineering, Delft University of Technology (Dates: Unknown)
  • Specialized in Software and Networking with a Computer Science orientation.
  • Master Thesis in Computer Vision and Deep Learning.
• Bachelor of Electrical Engineering, Shahid Beheshti University (Dates: Unknown)
  • Specialized in Electronics.
  • Ranked in the top 10% among the students in a class of 40 students.

Projects:

• Master Thesis at TU Delft (Dates: Unknown)
  • Conducted video classification on activity recognition datasets using various models.
  • Implemented a time-efficient video annotation tool using T-SNE and features similarity.
  • Increased annotation speed by 10 times compared to trivial video annotation tools.
• Advance Practical IoT (Dates: Unknown)
  • Implemented a real-time License Plate Recognition Android app and web API in a team of 2 people.
  • Used YOLOv3 object detection and network compression techniques in TensorFlow.
• Deep Learning (Dates: Unknown)
  • Implemented YOLOv2 for object detection and localization in a team of 5 people using PyTorch.
  • Applied network compression techniques for improving the speed of the detection process.
• Intelligent User Experience Engineering (Dates: Unknown)
  • Designed and implemented a support system for patients with diabetes type-2 on a Pepper humanoid robot using NAO SDK.
  • Demo project at Fruitpunch AI event with TNO collaboration about Hybrid Intelligence and Explainable AI.

Languages:

• English: Fluent (Toefl Score: 100)
• Farsi: Native