Position: Machine Learning Engineer
Category: Data Engineer
English level: upper
Years of experience: 2


Motivated engineer, skilled to solve data-driven tasks. Aimed to design and operate systems that connect data and business needs. Developed production-ready solutions combining scalable data processing, analytics and ML tools.

PROJECTS & EXPERIENCE
Develop and productionalise solutions based on data analysis and ML. Implement tools that bring value to the business or help teams in their activities.
News recommendation system. A distributed system that suggests what to read next. It uses data from GA in BigQuery and different ML algorithms. The system provides REST API, automatically updates when new data arrives and can serve multiple models or different model versions.  
Stack:  BigQuery,  AWS Batch, Nginx, Flask, Redis, Prefect, Grafana, Loki.

Web user matching system. Match site visitors in different sessions and for a long time (without cookies). Collect and stream user data to Kafka. Process it in real-time using a custom fuzzy matching algorithm and ML. Store data in S3 for further analysis and reports.  
Stack: Kafka, Redis Search (or Elasticsearch), Spark + Delta Lake, python Faust, FastApi, Prefect, Grafana, Loki, Prometheus.

Brand logo recognition. Detect predefined brand logos on thousands of photos. It is a scalable serverless batch processing system that uses custom trained YOLOv5 model to process large amounts of photos from S3. Implemented and curated all stages of ML pipeline: annotating tools set up; dataset management; model training and tuning; production recognition pipeline developing. 
Stack: CVAT, AWS Batch, PyTorch, ONNX, Spark, MySQL, Prefect.

Sentiment analytics. Bring sentiment analysis of user comments to BI. It is a serverless solution that fetches and processes user comments with NLP to predict their emotion and polarity. 
Stack: AWS Batch, Hugging Face, ONNX, MongoDB, Prefect.