lang-uk/recruitment-dataset-candidate-profiles-english huggingface dataset:
Position: Machine Learning Engineer (NLP)
Category: Data Science
Experience: 2.5 years | English: fluent

Completely designed and developed AI-driven document management system based on optical character recognition with further NLP of the extracted text.
Machine Learning Engineer proficient in Natural Language Processing, life-long learner with more than 5 years of R&D experience, 2+ years of commercial software development, PhD since 2014.
Kaggle competitions expert.
• Strong Machine Learning background and experience with open-source ML/math toolkits such as NumPy, Scikit-Learn, Pytorch
• Natural language processing expertise in Named Entity Recognition, Text Classification, Topic Modeling, Spelling Correction, Relation Extraction, Speech Recognition
• Extensive experience with open-source NLP libraries and toolkits
• Strong knowledge of Python, Linux and general software development workflow (source code management, debugging, testing, deployment)
• Experience with noisy and unstructured textual data
I would like to work in product companies only.
Position: Machine Learning Engineer | NLP | Data Scientist
Category: Data Science
Experience: 5 years | English: upper


I am Armen Mkrtumyan, a Senior Machine Learning Engineer with a proven track record in developing innovative solutions and conducting impactful research. In my current role at Podcastle, I am involved in various projects. I have successfully implemented filler word detection using Langchain and researched voice cloning models. I developed a pipeline that enables real-time custom text-to-speech at a human level, using only 70 sentences from the target speaker's speech. Additionally, I have worked on speaker separation models and contributed to the development of an internal data annotations tool for ASR model prediction evaluation. My leadership skills have allowed me to lead speech editing research and development efforts.

During my time at ML Labs as a Machine Learning Researcher, I conducted research in 3D object generation and trained a Generative Adversarial Network for augmented reality (AR) applications. I also explored the field of Brain Computer Interface (BCI) and utilized Convolutional Neural Networks to classify human brain activity using EEG datasets.

As a Data Scientist at Picsart, I made significant contributions to the marketing team by training a user subscription prediction model. This model effectively identified users with a high likelihood of subscribing to premium accounts, resulting in the lowest cost per subscription rate for advertisement campaigns. I also took on the role of mentoring interns and guiding their development of image clustering algorithms for automatically created Image Collections in the app. Furthermore, I played a key role in developing a data collection pipeline for an internal A/B testing tool.

During my tenure as a Junior to Mid-level Machine Learning Engineer at SoftConstruct, I successfully trained customer Lifetime Value (LTV) and Churn prediction models. Additionally, I developed an AI chatbot assistant called "Hoory" using Google's Dialogflow API.

In addition to my technical skills, I possess advanced proficiency in Python and have extensive experience with libraries and frameworks such as Numpy, Scipy, Scikit-learn, Pandas, Matplotlib, Seaborn, Pyspark, Databricks, Hadoop, PyTorch, TensorFlow, OpenCV, transformers, Spacy, Google Dialogflow API, and Langchain.
Position: Machine learning engineer, NLP DS
Category: Data Science
Experience: 9 years | English: upper


Data scientist and ML engineer with Python engineer background. 9 years of commercial experience in python development, 3.5 years of them in the role of ML engineer and data science field.
Bachelor degree in math and CS
Ready to quickly learn / adapt your technology stack
End to end - from data collection to customer success
Only interested in:
* remote first
* product company
* long term contract or employment
* stable funding record
* modern western culture

Not interested in:
* proxy agencies, secret projects
* business related with Russia
* industries: adult, adtech, blockchain, cryptocurrency, gambling, games
* DevOps responsibilities
Position: Machine Learning Engineer / NLP engineer
Category: Data Science
Experience: 3 years | English: fluent


Machine Learning Engineer with a solid background in Natural Language Processing, can speak several foreign languages, very quick learner with passion for learning new technologies and self-development. Ph.D. student.

1. Recruitment solutions project
Reduced time for development of a parser of Excel forms for new clients by 7x by developing state-of-the-art graph-based ML algorithm 
Implemented similarity search for vacancy-resume matching, created custom easily adaptable and extendable clustering algorithm
Developed graph-based page segmentation algorithm
Optimized inference speed of NLP models by 3x on CPU
Configured Google Cloud infrastructure and created data-lake for memory-efficient storage
2. SAP project (Safety data sheets management)
Developed a better performing ML solution to replace rule-based one to parse and validate information extracted from PDF documents
Established the whole ML pipeline: from data collection, supervision and synchronization of annotation process, organization of the human-in-the-loop process - to model development and with collaboration with backend team bringing it to production
Managed and supervised junior member of the team

- SaaS startup:
Worked with structured and unstructured text data, collected it from open sources, preprocessed, and used to improve the performance of the AI platform. Primarily classification problems. 

Experience with machine Learning libraries such as Scikit-Learn, PyTorch as well as good understanding of fundamental machine learning algorithms (both supervised and unsupervised).

Experience with open-source NLP libraries such as nltk and SpaCy, huggingface transformers, understanding of fundamental and new algorithms - words embeddings, RNN, LSTM, Transformers, BERT as well as practical experience in implementing them using state of the art NLP libraries such as transformers (also have experience with simpletransformers and Flair)
Experience with cloud solutions: AWS, GCP.
Make a long-term commitment towards working in an ambitious company focused on an international market. 
Have professional and career growth opportunities.

Not interested in BI Analyst positions.
Position: Machine learning engineer (preferably Computer Vision)
Category: Python
Experience: 2 years | English: intermediate


Data Science (02.2017-…)
Data Mining (02.2017-…)
Machine Learning (05.2017-…)
NLP (07.2017-…)
Deep Learning (05.2018-…)
Computer Vision (10.2018-…)


The last project was 'Sign language recognition' :
   - Object detection (YOLO model)
   - Sequence to Sequence Model (LSTM + Attention)


Other projects:
   - Topic modeling
   - Sentiment analysis
   - Anomaly detection
   - Other
Flexible working hours (Its a must, I like to start working from 07:30-08:00)
Possibility of partial remote work (preferably, but not a must)
Position: Machine Learning Engineer / Python developer
Category: Data Science
Experience: 3 years | English: upper

developed product that detects and recognize waste categories (build whole ML process started from data collection ending with modifying of trained NN);
implemented custom NER detectors;
implemented and trained speech-to-text models;
developed CV model for analyzing farm animals.
ML engineer (January 2021 - July 2022)
AI-based software and hardware solutions for waste sorting

Key responsibilities:
data collecting/processing/preparing to ML, training and deploying and modifying NN;
developing data collection and processing pipelines.

Tools & Technologies: Python (Flask, Tensorflow, Pandas, Numpy, Pyzbar, OpenCV), PHP 7, MySQL, MongoDB

Software developer (November 2019 - July 2022)
Software solutions for legal companies / departments to automate legal routine

Key responsibilities:
creating chatbots for internal automation / external use;
developing REST API as addition to low/no-code services, or for custom aims of clients;
making custom solutions for clients (as NLP projects, custom graph builders, data collection/processing);
implementing of open data systems, and creating additional tools to them;
integrating different client`s systems (such as CRM and different payment systems / bank accounts).

Tools & Technologies: No/Low-code services, Python (Flask, SpaCy, Graphviz, Pandas, Numpy, Matplotlib, Docker), PHP 7, MySQL
I expect to pursue a career as Machine Learning Engineer.
Position: Machine Learning Engineer / Python Developer (Computer Vision)
Category: Data Science
Experience: 3 years | English: fluent


================
WORK PROJECTS
================
++ Telecom ++
1.  Developed module for automatic exploratory data analysis of customer time-series data. 
2.  Participated in development of complex forecasting API that could analyze data, make predictions, visualize trends and - if needed - retrain model
Main libraries:
               - numpy
               - pandas
               - sklearn
               - seaborn
               - plotly

++ Advertising ++
1. Created a module for intellectual keyframe sampling for video processing, that used shot detection and scene transition techniques
Main libraries:
               - numpy
               - opencv
               - pyscenedetect
               - moviepy
2. Built a multimodal emotion detector framework (face detection and facial emotion recognition; voice emotion recognition, emotion in the text expressed).
Main libraries and models:
               - numpy
               - opencv
               - pytorch
               - facenet
               - kaldi recognizer
               - wave
               - librosa
               - scipy
               - keras
               - CNN
               - Huggingface Transformers
               - pydub 
3. Developed a module for speech-to-text conversion and automatic translation with speaker diarization function.
Main libraries and models:
               - numpy
               - kaldi recognizer
               - wave
               - librosa
               - google translate API
               - pyannote
4. Developed of logo detection and brand recognition modules.
Main libraries and models:
               - numpy
               - opencv
               - YOLOR
               - Resnet for muclticlassification POC
5. Developed patterns for dealing with NLP tasks (extracting ngrams, intelligent document/website parsing, etc.).
Main libraries and models:
               - spacy
               - nltk
               - regex
++ Other Responsibilities ++
 - Development and handling solution documentation stack.
 - Conducting analysis and developing requirements for data collection and labelling.
 - Developing presentations of teamwork and module demos.

===========================
PET AND VOLUNTEERING PROJECTS
===========================
1. Delivery robot with camera and sensors. Uses data from camera and sensors to ride on lanes and avoid collisions. Operates in ROS environment. Under development
2. Optical navigation for UAVs. Uses camera and IMU to operate under non-GPS envs.
Position: Machine learning engineer/quantative analyst
Category: Data Science
Experience: 5 years | English: upper

Startup from scratch - 24 microservices, Kuberbetes cluster, 2 databases, curator at time series data science course, electric unicycle big fan.
Timeseries Jedi. Advanced techniques in Deep learning, such as as the latest state of the art - DenseNets for noisy time series data forecasting, Ordinary Differential Equations as time series regressor, Wavelet transform to extract signal from noise, advanced financial time series (Marcos Lopes de Prado is my table book), have built startup from scratch for financial markets(24 micro services on Kubernetes cluster), timeseries course curator at Projector. I have my own features generation service, not dependable on data providers and data purity, I work with extremely noisy data with a very low degree of signal to noise ratio.
Timeseries or digital signal processing with a deep research part, no Dia city residents
Position: Machine Learning Engineer | R&D
Category: Data Science
Experience: 5 years | English: fluent

- The most amazing thing I've implemented is a neural network model that is better than a classical algorithm by 4x and is used in millions of devices.

- "Valued Innovator" recognition during my work in the R&D department of a big corporation

- Fulbright Graduate Student Program scholar (Computer Engineering Masters degree in the US)

- Personal project I'm most proud of - Human Activity Recognition and Classification based on Wi-Fi radio signals from a simple router. This work was then published as a paper.
Over five years of experience both in startups and big corporations. I have been working with Machine Learning-related projects for 3D сomputer graphics, human sensing (tracking, etc.), pattern recognition (edge-ML), and recommenders systems.

I'm passionate about learning and love challenging myself with new technologies and problems, so the more complex a project is, the more likely I will thrive working on it.
I'm looking for a challenging position that can allow me to combine various engineering and research experiences in Machine Learning and related areas.

I would be happy to work with something challenging and not ordinary(e.g., machine learning, R&D projects with some scientific part, hardware + software,  etc.)
Position: Machine learning engineer/ R&D engineer
Category: Data Science
Experience: 5 years | English: fluent

During my work I've:

** Created ML models training pipeline with experiments tracking 
** Developed emotion recognition system which is used now in production 
**  Created algorithm for human attention level estimation
** With colleagues created facial emotion analysis methodology
** Worked on writing installable modules for their further integration in analysis pipeline
** Created a prototype for key-word spotting
Machine learning engineer
* Master's degree in NTUU "Igor Sikorsky's Kyiv Polytechnic University"
* took part in developing a system for Ukrainian Telegram users authentication (so that Russian users couldn't send spam to Ukrainian Telegram bots)

I have worked on:
-  computer vision tasks (ML and classical approaches)
-  signal processing tasks (particularly, speech processing) 
-  creating ML training and monitoring pipelines
-  modifying existing pipelines in production cloud environment


 Particularly, my experience in both ML and classical algorithms includes the following tasks: 
-- 2d and 3d landmarks detection, 
-- human head 3D position prediction (roll/pitch/yaw values ),
-- face detection, 
-- talking face generation (GAN),
-- facial expression analysis and classification,
-- key-word spotting in speech signals,
-- audio signals classification,
-- text-to-image generation (fine-tuning)
Position: Machine Learning Engineer, R&D Engineer
Category: Data Science
Experience: 1 years | English: upper

I am curious and result-driven Machine Learning Engineer with strong background in Mathematics and Algorithms.
I was awarded in competitions:
-- ACM/ICPC
-- All-Ukrainian Student Olympiad in Informatics
-- Uzhhorod Summer Programming School
-- Huawei Honorcup Marathon 2
-- All-Ukrainian School Olympiad in Physics

My Licenses & Certifications in ML field:
-- Machine Learning (course on Coursera)
-- Deep Learning Specialization (5 courses on Coursera)
Projects
-- Producing Data-Driven Virtual Character Motion using Neural-Network-Based Autoregressive Model
-- Pose Estimation for Medical Exercises Adjustment
	
Responsibilities
-- Research state-of-the-art DL solutions
-- Develop, tune, evaluate and debug ML algorithms
-- Apply mathematical optimization for implemented algorithms
-- Conduct statistical analysis and visualize data
-- Preprocess and postprocess data
Position: Machine Learning Engineer / Researcher
Category: Data Science
Experience: 8 years | English: upper

Achived financial independendece by solving hardest data science tournament on the planet(stock market)
About 15y since im coding.
9y since im doing commercial coding
About 5 years since I started training neural networks and moved to data science.

Main fields of my interest is quantitative finance, computer vision, nlp, reinforcement learning
I like to work in startups, solve real users problems, making research, working on challenging problems.

Areas i
Position: Machine Learning Engineer / Researcher
Category: Data Science
Experience: 4 years | English: upper

- Energy Consumption Hackathon. 1st out of 31 participants. Predicted consumed energy for each unit given.
- Preprocessed raw data of questionable consistency and high number of entities to create a dataset usable for modelling
- Prepared EDA, visualised results and generated insights from data
- Pipeline for face recognition + emotion recognition in real time + pushed code into production
- Research and demo preparation for hand washing detection model
- Incoming messages classification to direct for the corresponding department + pushed code into production
- Position sizing for investment strategy using Reinforcement Learning algorithm
No data analytics positions
Position: Machine Learning Engineer/Researcher
Category: Data Science
Experience: 3 years | English: upper


Object Counting From Video:
• Collaborated with a team to ensure the success of the project.
• Conducted experiments and fine-tuned the algorithms for optimal performance.
• Counted the number of products taken out of a fridge.
• Developed a computer vision system.
• Documented the system design, methodologies, and results.
• Implemented YOLO, SORT, and Deep SORT in Pytorch.
• Presented findings and recommendations to stakeholders.
Position: Machine Learning Engineer / Research / NLP
Category: Data Science
Experience: 3 years | English: intermediate


- Generation and testing of hypotheses.
- Development of data processing and analysis pipelines.
- Training and testing NLP models for Android.
- Development of models to optimize the operation of mobile devices.
- Participation in writing scientific articles in peer-reviewed journals.
Position: Machine Learning Engineer / Software Engineer
Category: Python
Experience: 7 years | English: fluent


I have interned and worked as an R&D software developer in one sense or the other since 2015. Mainly specialized in computer vision algorithms` both classical and based on deep neural networks. I am also interested in just building useful and scalable software.  My latest and most relevant experience is during the past 3 years at SuperAnnotate where I’ve been researching SOTA ML solutions for Image Segmentation, object detection and tracking tasks. Used various convnets and vision transformers. Written microservices that would use these models to help our customers and have handled their CI/CD pipelines. In terms of software engineering I’ve worked on designing automation pipelines and an open source SDK. More previously learned and implemented classical CV functionality such as importing and exporting images in various formats, morphological operations, keypoint identification, etc. 
My main interests now lie in deep learning, the possibility of training multimodal neural networks and LLMs.
Position: Machine Learning Engineer (switching from Software Developer in Test)
Category: Data Science
Experience: 5 years | English: upper


I am always in search of something new and exciting. As I am not limited by the positions definitions, my search has led me to Machine Learning. (I used to work as a Software Developer in Test before.)
Although I am quite new to the field I have a great experience of working with challenging tasks, different domains in beautiful teams of different sizes and compositions and also by myself.

My Experience:

SQUAD, Kyiv — Software Developer in Test
March 2021 - June 2022
Research and delivery team working on the latest smart home security/IoT products.. 
Developing test automation framework and supporting infrastructure using Java, Python,  Appium, NReport Portal, AWS  S3, AWS Athena, Tableau, TestNg, TestRail, Jackson, Jira, Confluence, Git (GitLab, Bitbucket), JFrog, nrF Command Line Tools, J-Link.
develop and analyze automated tests for mobile app and separate IoT devices.
provide improvement to existing TA framework architecture and external libs with Java and Python.
flash, update and mock actions on IoT- devices  with J-Link and RPi.

EPAM Systems, Kyiv — Test Automation Engineer
Sept 2017 - March 2021
Technology company that offers software engineering services for software development and digital platforms. 
Worked on several projects writing Auto-tests and creating framework for Test Automation, creating supporting infrastructure using Java, Python, Groovy programming languages. Used technologies and Aps: JUnit, TestNG, Spring, Appium, Hybris, MySQL, Jenkins, Jira, Confluence, Git (GitHub, GitLab, Bitbucket), ReportPortal.
develop and analyze automated tests for mobile app, Backend, Frontend using Java, Python, C#, Groovy.
create test automation framework architecture from scratch.
organizing test automation activities.
- Exciting challenging tasks in an interesting domain
- Supportive friendly motivated team who will help me to improve my skills
- Freedom of proposing and developing new ideas
- Work-Life balance
- Self development opportunities
Position: Machine learning engineer / Tech lead
Category: Data Science
Experience: 6 years | English: upper

3 US patents in deep learning.
ML projects:
-face swap
-makeup swap
-trading algorithm based on reinforcement learning (Proximal Policy Optimization)
-object segmentation/detection/tracking
-subframe generation (slow-motion effect in videos)
-semantic search engine in text corpora
-speech recognition
-background removal from photos and videos
-optical character recognition (OCR) in PDF documents
-assistant chatbot with GPT-4 backend
-image retouching
-virtual jewelry try-on
-financial time series forecasting
-recommender system for e-commerce
With  6+ years of commercial experience in data science, I have developed a strong proficiency in AI/ML, specializing in computer vision, NLP, time series forecasting, and reinforcement learning. I am conversant with every stage of data science project life cycle, from research, data collection, and normalization to algorithm coding, training, evaluation, and deployment into AWS/Google Cloud or on Android/iOS devices. My experience in leading a data science team has honed my technical leadership skills, reinforcing my ability to drive AI/ML projects to successful completion. Outside my professional endeavors, I've channeled my passion into a hobby of programming crypto trading algorithms based on reinforcement learning.
Interesting and non-trivial tasks in the machine learning and deep learning fields,
flexible working schedule,
remote work,