Verified Expert in Engineering
Machine Learning Engineer and Developer
Stefan is an experienced machine learning and machine learning operations (MLOps) engineer with hands-on experience in big data systems. His demi-decade of expertise is supplemented by a master's degree in artificial intelligence. Stefan has worked on problems such as object detection, classification, sentiment analysis, named-entity recognition (NER), and recommendation systems. He is always looking forward to being involved in end-to-end machine learning projects.
PyCharm, Python 3, Python, GitHub, Amazon S3 (AWS S3), JSON, Distributed Systems
The most amazing...
...end-to-end machine learning solution I've created optimized the cost of the machine learning pipelines numerous times with state-of-the-art results.
PepsiCo Global - DPS
- Implemented an end-to-end pipeline using PySpark machine learning pipeline.
- Implemented CI/CD with unit and integration tests using GitHub actions.
- Implemented Spark and scikit-learn/Pandas ETL jobs for handling large volumes of data (150 TB).
Tech Lead Data Engineer
- Led a small team in implementing an ELT pipeline to get data from a GraphQL database and put it into Azure SQL. Everything was Dockerized and pushed to Azure Image Registry.
- Implemented KPI calculations using PySpark, which was communicating with Snowflake. Defined table schema for Snowflake and created migration scripts.
- Followed the Scrum methodology, including daily scrums, retro, and planning, and used Jira.
- Led a small team in implementing ETL Spark jobs with Apache Airflow as an orchestrator, AWS as infra and Snowflake as a data warehouse.
- Carried out deep learning model optimizations using quantization, ONNX Runtime, and pruning, among others.
- Monitored model performance, including memory, latency, and CPU usage.
- Used Valohai to automate the CI/CD process and GitHub Actions to automate some parts of the MLOps lifecycle.
- Created automated experiment tracking using Amazon CloudWatch, Valohai, Python, GitHub Actions, and Kubernetes.
Machine Learning Engineer
- Optimized a machine learning compiler already on a trained network without re-training using Open Neural Network Exchange (ONNX) and implemented custom operators using PyTorch and C++.
- Worked on an Android machine learning solution and mentored a less experienced developer to train and prepare an object detector and classifier to run smoothly on an Android device.
- Enhanced a project that aimed to upscale images to be as perfect as possible toward 4K resolution.
- Involved in SDP of ship routing problem. Implemented an algorithm from scratch that will guide the ships. Fuel consumption and ETA were used for calculations.
- Worked on open source ONNX Runtime in order to add support for the MIGraphX library.
Machine Learning Engineer
- Contributed to complete MLOps lifecycles using MLflow for model versioning, LakeFS for data versioning, AWS S3 for data storage, and TensorFlow serving in Docker.
- Functioned as a data engineer using Apache Spark for ETL jobs with Prefect and Apache Airflow for scheduling.
- Trained several different architectures for object detection and classification.
Machine Learning Engineer
- Scraped product information from various websites, then analyzed and prepared the scraped data for web shops using natural language processing—long short-term memory (LSTM), Word2Vec, and transformers—and added NER since the data was in Serbian.
- Used Amazon SageMaker to automate the machine learning pipeline—data preprocessing, model training, and deployment. Executed automated retraining and deployment of the model, completing the machine learning process before the client updated new data.
- Worked on big data projects using Apache Spark, Kafka, Hadoop, and MongoDB.
- Worked as a data engineer using Spark to create optimized ETL pipelines. Translated the client's needs into SQL.
Automated End-to-end (E2E) Computer Vision Solution
• Detecting objects in the room
• Classifying person poses
• Automated re-training (active learning)
• Model and data versioning
• Dockerized pipeline
Using those models and predictions, we created a post-processing pipeline for creating reports or key performance indicators (KPIs) for clients.
Android COVID-19 Test Classification
I led a team of two people on this project. We used MobileNet due to size, and all business-relevant metrics were great. We used many optimization techniques to deploy the model to Android, such as quantization, pruning, and knowledge distillation.
Image Super Resolution
• Optimized solution to reduce cost and calculation time.
• Scheduled jobs via Airflow and Prefect.
The tech stack was: Spark, Scala, AWS S3, Kafka, Apache Airflow, and Prefect.
NLP Articles Processing
1. Find all relevant tags (events, locations, names, etc.) in the article.
2. Find pairs of tags that are somehow related.
Hugging Face transformers were mainly used to tackle this problem (BERT-based models). Overall metrics were above 95%.
Tech Leadership for the DE project
Python 3, Python, Scala, Java, SQL, Snowflake, GraphQL
Spark, Apache Spark
PyTorch, Keras, NumPy, Scikit-learn, TensorFlow, Pandas, PySpark
PyCharm, GitHub, Pytest, Amazon SageMaker, Codeship, Apache Airflow, AWS Glue
Data Science, ETL
Amazon Web Services (AWS), Jupyter Notebook, Visual Studio Code (VS Code), Docker, Kubernetes, Amazon EC2, Apache Kafka, Azure, Databricks
Amazon S3 (AWS S3), JSON, Databases, NoSQL, MongoDB, Data Pipelines, Azure SQL
Deep Learning, Machine Learning, Artificial Intelligence (AI), Computer Vision, Natural Language Processing (NLP), Natural Language Understanding (NLU), Convolutional Neural Networks, Recurrent Neural Networks (RNN), Machine Learning Operations (MLOps), Neural Networks, AI Design, Deep Neural Networks, Software Engineering, Technical Hiring, Source Code Review, Code Review, Task Analysis, Interviewing, GPT, Generative Pre-trained Transformers (GPT), Data Engineering, Recommendation Systems, Open Neural Network Exchange (ONNX), Lens Studio, Optimization, Team Leadership, Valohai, Time Series, Data Modeling, Data Mining, Monitoring, Big Data, Image Processing, Transformers, Cloud, Object Detection, Computer Vision Algorithms, Object Tracking, Web Development, Speech Recognition, Voice Recognition, Cloud Services, ETL Tools, Distributed Systems, Hugging Face, BERT, Back-end, APIs, Software Architecture
Master's Degree in Artificial Intelligence
University of Novi Sad - Novi Sad, Serbia
AWS Certified Machine Learning - Specialty
Amazon Web Services