lang-uk/recruitment-dataset-candidate-profiles-english huggingface dataset:
Position: Machine Learning Engineer
Category: Python
Experience: 2.5 years | English: fluent


ML engineer at MiraMedix: 1 year 
*developed and supported a multilingual system of machine translation models for medicinal texts
*developed a service for clinical trials for patients with cancer
*developed a content pipeline for "smart medical document storage" mobile app 
Stack: Python, PyTorch, FastAPI, Docker, Celery, RabbitMQ, PostgreSQL, Alembic, transformers, spaCy

Analyst at BORZO and Zyfra: 2 years
*developed an automated reporting system for all 9 countries of company's presence that demonstrated main metrics
*developed a geospatial heatmap visualization of main metrics for all 9 countries of company's presence
*debugged and supported the system for oil delivery distribution on oil production plants
Position: Machine Learning Engineer
Category: Data Engineer
Experience: 2 years | English: upper


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.
Position: Machine Learning Engineer
Category: Python
Experience: 4 years | English: upper

Vast experience of developing enterprise desktop applications with complex architecture.

Interested in machine learning, so studying deep learning frameworks and technologies, working on pet projects in the field.
.NET, C#, C++, VB6, Oracle, Arcgis, Geodatabases.
Working with machine learning and neural networks projects.
Position: Machine Learning Engineer
Category: Data Science
Experience: 1 years | English: upper

Programming for Everybody (Getting Started with Python) | 2022
Create Your First Python ProgramFromUST |2022
Regular Expressions in Python | 2022 Overview of Data Visualization | 2022
Create Your First Web App with Python and Flask | 2022
Data Visualization using Plotly | 2022
Create a Landing Page using Mailchimp | 2022
Exploratory Data Analysis with Seaborn | 2022
Python Data Structures | 2022
Using Databases with Python | 2022
Using Python to Access Web Data | 2022
Capstone: Retrieving, Processing, and Visualizing Data with Python | 2022
Project Scheduling: Estimate Activity Durations | 2022
NLP model selection and application;
Classification and categorization of documents by blocks and keywords;
Employing Github and Gitlab to manage the project;
Utilizing DBeaver to operate on large databases;
Using Docker for Tests and Deployment;
Position: Machine Learning Engineer
Category: Data Science
Experience: 1 years | English: upper

Neural network and Machine Learning courses - university (NN and ML repos in github);
Diploma project: implementation and analysis of natural computing algorithms (harmony search, PSO, simulated annealing) for hyper parameters optimzation using Optuna framework;
ML, python and statistic certificates;
Dataquest (data analyst) course certificate;
applied in LinkedIn;
Node js Developer - 6 months
Salesforce Developer - 1.5 year (SF developer certificate); 
Data Analyst trainee - 3 months;
Data Scientist  - 7 months;
Currently: Deep Learning\ Software engineer - 6 months;
Position: Machine Learning Engineer
Category: Python
Experience: 2 years | English: upper

Computer Vision, data generating, data processing, data labeling, processing datasets, automation of manual processes, and comprehensive experience with different deep network architectures. Taught me to learn on the fly, so unfamiliar tasks are my field of interest.
Now, I have two years of experience in the Data Engineer position, so I have a lot of experience in data processing/labeling non-tubular data - images, videos, also tubular data, etc.

My duties also included training, creating, versioning ML models, creating inference pipelines, and many relative tasks. Also automating many data preparation steps.


My machine learning experience is based on Pytorch projects, so I developed models from scratch to get an understanding of processes in the middle of it. 
I'm experienced in object detection, classification, and segmentation tasks. Last time I'm working on using and tuning generative models such as StableDiffusin for production causes, which is also my bachelor's diploma theme.

In commercial tasks, I've done the full pipeline - from collecting data to training and optimizing models.
I also made a lot of research on different tools for optimization and acceleration of development processes.
It would be a great opportunity to become a part of a production company with a great team and exciting products which I can be a part of.
Position: Machine Learning Engineer
Category: Python
Experience: 0 years | English: fluent

• TensorFlow Developer (Deep Learning AI)
Completed 4 courses on Deep Learning, Computer Vision, NLP, and Time Series.
• SPF HACKATON 2023 May 2023 o I have been a team leader. Our solution took the 1st place.
• Deep Learning Specialization (Deep Learning AI) October 2022 o Completed 5 courses on CNN, Recurrent NN, LSTMs, and Transformers.
• SQL Course (University of Michigan) May 2022
• LeetCode (130+ solved problems) January 2022 - Present
OCR for Differential Equations, group project December 2022 • Used Python and Tensorflow to preprocess images of separate symbols and fine-tune the
InceptionV3 model for classification to solve differential equations. Achieved more than a
0.95 F1 score.

Cryptocurrency price prediction, personal project April 2023
 • Collected dataset, used Python and PyTorch to fine-tune FinBERT model to analyze
sentiment for tweets. Used Tensorflow to build the LSTM model and predict the next price
based on price history and sentiment. Achieved 0.002 RSME.

Object detection and tracking, group project May 2023
 • Collected and labeled a dataset to detect objects on UAV videos. Used Python and PyTorch
to train the YOLOv8 model. Sophisticated techniques from OpenCV trackers to DeepSORT have been used for tracking objects. Achieved more than 95% recall, mAP50-95 – 78%.
I just want to create something great.
Position: Machine Learning Engineer
Category: Data Science
Experience: 1 years | English: upper

I completed several courses:
1. Coursera’s “Mathematics for Machine Learning: Linear Algebra” offered by Imperial College London.
2. Coursera’s “Machine Learning” offered by Stanford
University.
3. mlcourse.ai open Machine Learning course by Yury Kashnitsky.
4. Coursera's "Neural Networks and Deep Learning" offered by DeepLearning.AI.
5. Coursera's "Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization" offered by DeepLearning.AI.
6. Coursera's "Version Control with Git" offered by ATLASSIAN University.
OneReach.ai, Junior Solution Builder - 6 months
Our team was maintaining and developing the LiveAgent application for Call centers. We mainly dealt with the backend part but sometimes participated in front-end development. As part of a team, I was responsible for incorporating new features, manipulating the data, migrating the existing solutions to the latest versions of libraries, and developing/updating the visualization tools for plotting various statistics over time, for example, the agent call duration for the last day, week, month, etc.
Winstars Technology R&D, Data Science Intern - 3 months
I was working on an instance segmentation task on the PASTIS dataset. I have chosen the Mask R-CNN architecture as the starting point and faced several related issues. The main problem with Mask R-CNN is that it operates on independent images, whereas PASTIS consists of image time series. The architecture is composed of 2 parts: the backbone and heads. The former acts as a feature extractor, generating single or multiple feature maps. The latter performs classification, regression(bounding box prediction), and segmentation(binary masks for every instance). In order to overcome the issue with data, it was decided to change the backbone. I have experimented with different approaches, but the one which worked out for me was U-TAE.
I`m very enthusiastic person and I have a great passion concerning ML, so I`m looking for a position to work on a challenging tasks, so that I can gain a new knowledge as well as interesting experience and get acquainted with latest insights in this field.
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper


Over the past three years, I have gained valuable experience in implementing and conducting research in various Machine Learning and Deep Learning topics, with a primary focus on Computer Vision. Throughout this time, I have worked on several projects that allowed me to develop a strong understanding of the field and the latest technologies and frameworks.

- In my current role, I have been responsible for building and improving several models for a variety of applications, including Generative AI, Text-to-Image, Face Recognition, Image Tagging, NSFW Detection, GANs, Diffusion models, and more. These projects have involved a range of complex challenges and have required me to stay up-to-date with the latest developments in the field to ensure that I am delivering the best possible solutions.

- As a part of my responsibilities, I have also delivered several Deep Learning solutions that replaced third-party services, leading to significant cost savings for the company. 

- Additionally, I have optimized model efficiency for several services by using different Deep Learning techniques. These kind of model improvements resulted in 40% cost savings for the maintenance of these services.

- By successful cooperation with MLOps team, I have built end-to-end CI/CD pipelines for several projects, resulting in faster integrations and increased workflow efficiency.

- I also have conducted large-scale distributed trainings on the cluster.
Position: Machine Learning Engineer
Category: Python
Experience: 5 years | English: fluent


Participated in the full development and Refactorization cycle of the fully clouded and deployed product: continues
serverless integration, test-driven development, workflow and data pipeline infrastructure, API, auto-scaling
 Implemented custom fully convolutional network architectures and loss functions using TensorFlow/Keras to detect
objects from multispectral Satellite Imagery: Forest, Bush, House, GreenHouse, Vehicle
 Established custom fully General Adversarial Networks to improve multispectral low resolution to Super Resolution
 Conducted extensive literature reviews on topics of semantic segmentation, GANs, transfer learning, and self-supervised
learning using Pytorch
 Provided Solutions the to optimize Training procedure of Segmentation of satellite imagery 5 times and gain in storage by 80%
 Interviewed over 70 candidates, and led 5 intern
 Used Technologies and Frameworks: Google Cloud, Airflow, FastApi, Tensorflow, Pytest, Docker, SQL, Kubernetes, Pytorch
I would prefer to join a team where the tasks are not repetative and doesn't envolve any challenge and growth in knowledge. Respect and skill sharing between team members are also crucial things
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: fluent


Passionate about solving problems with AI. Happy to be immersed in data.

- 3 years of industry experience in data analysis, designing and building Machine Learning solutions 
- end-to-end ML projects development, from research and development to production
- maintainable code development, CI/CD, code review

Domains:
 - Computer Vision: image classification, object detection and tracking, semantic / instance segmentation, OCR, face recognition
 - Audio: speech-to-text, speaker recognition and diarization, voice recognition, music transcribing, instruments recognition
 - Healthcare: time series analysis

Tools: 
- Python
- Linux shell, bash scripting
- GitLab CI/CD
- AWS EC2, S3
- PyTorch, TensorFlow
- Streamlit
- NumPy, pandas, sklearn
- matplotlib, seaborn, plotly
- Jupyter
- Docker
- FastAPI
Position: Machine Learning Engineer
Category: Data Science
Experience: 0 years | English: upper

12th place of my team in the DS NGWL Hackathon.
Passionate machine learning enthusiast and data-driven analyst with the ability to apply
ML techniques and leverage algorithms to solve real-world problems. Gained knowledge
and some experience on Khan Academy, Coursera, Kaggle, Udacity. Have Political Sciences background (have a PhD).

Studied Maths (Algebra, Linear Algebra, Intro to Statistics and Probability, Calculus) on Khan Academy from March to July (2020);
ML and DL on Coursera, Google, Udacity, Kaggle (July - October, 2020), SQL in November, Computer Vision in progress.

Projects:
NGWL.Hack predict churn problem (McKinsey&Company):
Collaborated to the team on competition by adjusting the model development from 69% to 75,5%. Handled missing values and tuned the algorithm, reaching top 12 teams on the
leader-board.
Time Series Analysis projects (Britannia on 'NIFTY-50 Stock Market Data' and Visa on 'New York Stock Exchange Data'): implemented modern time series prediction techniques such as Facebook's Prophet and Neural Prophet, LSTM.

Breast Cancer Image Classification: trained classical ML algorithms and improved the CNN model for getting 81% accuracy on validation data having a small data set.
EDA and Data Visualizations on 'Suicide Rates Overview 1985 to 2016' and 'Summer Olympics Medals (1976-2008)'.
Tinkoff Hackathon task 'Who will like the story?': successfully applied NLP techniques and Feature Engineering that demonstrated the highest (0.44) score on the task metric.
Titanic: achieved 0,80861 model accuracy that gained top 3% on Kaggle leader-board.
Housing Prices Competition for Kaggle Learn Users: reached top 4% on Kaggle leader-board using Regression Analysis.

My strengths: analytical thinking, hardworking, meticulousness, thirst for knowledge, communication.
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper

- Successfully delivered all the projects.
- Published article on kdnuggets
- Successful presentations on international conferences such as Photonics WEST (USA), IONS Yerevan (Armenia)
- Preprocessing and cleaning large datasets to prepare them for multi-label classification problem, for NER problem, for Retrieval Augmented Generation problem;
- Evaluating and selecting appropriate ML techniques and models such as transformers with different losses;
- Training, fine-tuning, and validating ML models;
- Optimizing and deploying ML models to production environments;
- Building retraining pipeline for the final model;
- Explaining and interpreting model outputs and results to non-- technical stakeholders;
- Preparing the report and writing the article for publication.
- Prompt Engineering for the LLMs on Question-Answering task;
-Conducting A/B testing and experimentation to evaluate and compare different ML models or algorithms;
- Building step function pipeline;
- Released the framework that understands the accuracy of predicted measurements, sizes and 3d avatars;
- Worked closely with US actual data from scanners. Designed algorithms to track and detect anomalies in multiply data, which allowed tracked about 85% of unhealthy data;
- Implemented model versioning and deployment strategies to manage the lifecycle of ML models;
- Developed the monitoring system for a whole pipeline.
- Designed a model in pilot to increase marketing resources for more than 6 large USA brands;
- As a part of Data Engineering jobs setup correct ETL processes with Amazon EMR. This helped client to economy more than 40 % in money terms and gave the win in time processing more than 10x;

Solutions: NLP ( Multi-Label text classification, NER with SOTA FLAIR approaches, Document Q&A System using Langchain / OpenAI) , CV ( Object classification (YOLOv8)), classical ML ( Regression Task, Clusterization Task)

Technologies: Python, AWS (Sagemaker, Step Functions, ECR, S3), Weight and Biases,
Data Version Control, HuggingFace (transformers), FLAIR, Spacy, PyTorch, OpenAI, Docker,  LangChain, Vector Stores (FAISS, Chroma)
- Opportunity of professional growth;
- Flexible schedule
Position: Machine Learning Engineer
Category: Data Science
Experience: 1 years | English: fluent

Wild Wild Hackathon winner in gamedev
category
Finalist in CIG R&D LAB
Winner in Creative Law Studios Hackathon
Finalist in Hack for Locals 2.0, project ReCOVisor
Innovation Spring, project BB-8

PAPERS
Application of semantic segmentation of clouds
of points for preservation of cultural heritage
CITRisk’2021: 2nd International Workshop on Computational & Information Technologies for Risk-Informed
Systems
Classification of chest x-ray images using CNN
PricewaterhouseCoopers (PwC)
Junior Data Analyst (Slovakia)
Apr 2021 - Present
• Developed model for semantic segmentation
of satellite images
• Analysed data for outliers in farmers’ data
• Tested Rust for scalability in TimescaleDB,
QuestDB, Kafka
• Created python scripts for geospatial data
analysis, visualization

Trainee Data Analyst (Ukraine)
Dec 2020 - Mar 2021
• Used Excel for data analysis
• Used SQL for data preparation and transformation to the common data model
• Used python and Alteryx for process automation
Position: Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: fluent


Primarily worked in fintech industry in one of the largest banks in Kazakhstan. My current team projects mainly involve computer vision tasks and analysis. 
Throughout 2 years at Kaspikz, I have developed a keen understanding of SQL, Python, and Computer Vision.
Position: Machine Learning Engineer
Category: Python
Experience: 3 years | English: fluent


• Processing of a huge amount of street view imagery collected mainly from Milan and some other cities.
• Actively involved in data preprocessing, model development, deployment, and monitoring.
• Deployed a model for anonymization of faces and car plates according to GDPR requirements.
• Worked on the whole development cycle of models for detection and classification of clothes, also
attributes of them such as color, texture specifically with low quality images.
• Worked on Virtual Try-on to handle class imbalance for classes with low samples, at the same time
tackled segmentation of body parts and clothes.
Position: Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: fluent


- Produced data processing and testing pipeline for financial trading system and optimization of its algorithms. Used statistical approaches to introduce some guarantee for optimization.
- Created plant image recognition service for experts in the field.
- Developed Client-Server application for large hospitality business in C++/JavaScript.
- Worked on improvement of open-source DNS that's used in several countries
- Created a smart faceted search solution for company internal sources
- Built an ETL pipeline with NER, PoS and Deep Learning
- Built a cloud infrastructure for text documents classification on AWS services
Position: Machine Learning Engineer
Category: Data Science
Experience: 6 years | English: upper

- Delivered image classification system with 10,000+ classes, under business constraints such as: low quality and quantity of data; real-time response time; high accuracy requirements.
- Delivered multitasking SaaS core used in similarity search, image classification and recommendation engine, managed it as a product owner and successfully presented to external investors, which led to the next investment round for a company.
Proficiency in Python, high expertize in classical Data Science and Computer Vision.

Worked on several ML products
[recommendation engine, similarity search engine, image classification, image enhancement, pose estimation ...] which includes:
- Research and reproduction of state-of-the-art scientific papers
- Establishing analytics and hypothesis checking workflow
- Product design & problem research
- Prototyping MVP and delivering final production-ready products
- External team management (2 people)
- Communication & presentation for stakeholders.
Full-time
Looking for startups (seed, A round+), product companies.


!No outsource/outstaff
!No relocate
!No data analytics (BI, google analytics, etc.)
!No VR, AR, Blockchain
!No dating/gambling/etc