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).

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.

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.

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.

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.