Position: Machine Learning Engineer
Category: Data Science
English level: upper
Years of experience: 2.5

- Master's deegre in System Analysis
- Writing personal tech blog in the medium platform
- The finalist of a hackathon FTI_HACK in 2022
- The 3 place in a hackathon INT20H in 2021
I have a solid background in the development of ML/AI solutions.  
I have participated in the whole CI/CD in the ML solution. I have creted definintion for labeling team, extracted data from DB, scrapped web pages, aggregated data after the labeling process, trained language models, set up MLOps practices into the project, built and implemented knowledge graph structure, researched, etc.

# In the last Project my responsibilities and achievements were
- During project we as ML team developed security product from scratch.
- Data Cleaning/Aggregation/Analysis, as result I have developed data aggregation and preprocessing pipeline for NER task that improve model accuracy on 15% 
- Train transformer base model for NER task with Spacy 
- Research Slack and M365 APIs, after it prepared data scheme and initial fraud detection implementation POC using knowledge graph which were moved into production
- Proposed and implemented MLOps architecture for model training process which reduce team's time consumption (from Data Labeling to  Model versioning)
- Prepared model fine-tuning for fraud detection with 0.98 f1 score
- found and fixed bug which lost important information and reduce prediction score, so improved overall accuracy on 5%
- Research science papers and implement ideas into real-life
- Created architecture and developed a data anonymization pipeline on medical conversational data
- Web Scraping for the creation a dataset
- Extracting text, and images from PDF, Scans, docx
- Generation dataset using LLM: GPT-3, GPT-3.5, Falcon, AI21Labs, etc
- Preparation of demos version for the customer about POCs and  implemented improvements
- Used asynchronous packages for working with huge amounts of data such as Dask and Ray
- Support the DevOps team while setting ML related tools on Kubernetes

=========================================
* ML Tools
Spacy, Neo4j, PyTorch, TensorFlow, HugginFace, MLFlow, Pachyderm, DVC, Weight&Biases, LabelStudio, Gradio, StreamLeat, Scikit-Learn, Padas, Numpy, Dask, Ray

* Cloud
AWS, Kubernetes, Kubeflow, Docker

* Non-ML Frameworks
Oracle, ElascticSearch, Cassandra
Consider ML-based solution