Position: Machine Learning / MLOPS Engineer
Category: Data Science
English level: upper
Years of experience: 5

Applied mathematic master degree: optimisation, probability theory and statistic, numerical methods, control theory.

Extremely qualified in deep learning and have experience with different solutions for common problems: imbalanced datasets, overfitting, gradient vanishing. 

Computer vision: Worked with state of the art architectures for object classification, detection, instance and semantic segmentations: EffNet, ResNet, Unet, Mask-R-CNN.

NLP: Worked with GLOVE and Transformer based models for text classification, NER, similarity recognition.

Designed and deployed a vast amount of custom architectures for  different domains: CNN; LSTM; RNN; Attention; mixed image, text and table inputs. 

Experimented with variational, graph and fast CNN models. 

Experienced in various classical machine learning techniques (SVM, DTC, PCA, TSNE, Bayesian models)

Certificates:

2020
Data Engineer Nanodegree
2019
Advanced Machine Learning with TensorFlow on GCP
Data Engineering on GCP
From Data to Insights with GCP
2018
Self-Driven Car Engineer Nanodegree
2017 
Machine Learning Engineer Nanodegree
Results:

2021-2022:

Deployed several TF extended pipelines for automative model training conveyors via Kubeflow on Premise and on GCP and Airflow. 

Trained, modified and deployed (async http, docker):

- several models with transformer architecture for name entity recognition. 
- image classification and model with Effective CNN as a backbone 
  model.
- adverts and texts similarity classification models. 
- MaskRCNN for watermarks detection. 

I was responsible for full process of model development: 

- EDA
- experiments with different model architectures 
- deployment
- worked closely with data moderation team for model validation 
   before deployment. I also developed datasets for model training 
   and improvement.  

2020 - 2021:

- customer clusterization models improvement and deployment via 
  AWS Sagemaker and Glue.
- OCR models based on variational multitask autoencoder 
   architecture.
- RNN model for estimating crop yields via satellite images of 
   vegetation indexes.  

2018-2020:

Responsible for full process of deep learning research and deployment of different models:

- advert categories classification based on texts and images neural 
  networks. Deployed them via TF Serving.
- various deep and bayesian models for marketing fraud detection: 
  TTI-injection, spam, bots. 
- scam detector based on user experience and advert metrics. 
  Worked with downscaling, upscaling, weighting due to a high 
  imbalance of dataset.
- CTR predictor of adverts based on attributes, content, prices, and 
  regions. Worked with GLOVE embedding, and custom loss 
  functions. 
- U-NET autoencoder for finding texts on images.

Android developer: 2013 - 2018.