lang-uk/recruitment-dataset-candidate-profiles-english huggingface dataset:
Position: Machine Learning Engineer/Data Scientist
Category: Data Science
Experience: 4 years | English: upper

Working in Machine Learning for ~4 years, and ~7 years in IT in general. I have experience with long-term ML projects in production, as well as with POCs, demo, MVPs, projects from scratch, optimization projects. 
I worked in teams, as well as only ML engineer on the project, so I will have no problem in communication with team, as well as with clients.
Projects with Computer Vision:
 - building detection, classification, clustering models
 - deep learning models: ResNets, SSD models, RCNN and maskRCNNs, Unet, Yolo
 - building convolution models from scratch
 - working with cameras, calibration, etc
 - object tracking with kalman filter
 - image preprocessing techniques
 - some model quantization experience

Project with NLP:
- Text Classification: vectorizing texts, topic classification with classic ML algorithms, LSTMs
- Building small chat-bots
- QA systems
- Text predictions

Small projects:
- Predicting WI-FI signals model depending on other features of routers for predicting failures
Looking for full-time jobs or part-time fixed projects
Position: Machine Learning engineer, Data Scientist, Computer vision engineer
Category: Data Science
Experience: 6 years | English: upper


This is a list of tasks and data I worked with:
Classification, segmentation
Face recognition
Pose Estimation
Gesture recognition
Augmented Reality and Virtual Reality 
Surveillance system project(alarm detection)
NER
Depth data
Lidar
Satellite data/Aero data
Interesting project
Professional growth
Position: Machine Learning Engineer/ Data Scientist/ Computer Vision Engineer
Category: Data Science
Experience: 5 years | English: fluent


Worked in product companies mostly on Computer Vision projects. Have an experience of creating product architecture from scratch. Successfully launched MVP. Have implemented own machine learning model that solved specific problem.
Position: Machine Learning Engineer/Data Scientist/Python Developer
Category: Python
Experience: 2.5 years | English: intermediate

Courses:
- Web Development in Python & Django - ​http://itbursa.com/python
- Software Development Processes -
 https://www.udacity.com/course/software-development-process--ud805 
- Andrew Ng, Machine Learning - ​https://www.coursera.org/learn/machine-learning/ 
- Machine Learning A-Z - ​https://www.udemy.com/certificate/UC-C6DVIDAP/ 
- Machine Learning Crash Course with TensorFlow APIs - 
https://developers.google.com/machine-learning/crash-course/
Python Developer and Data Scientist at ActiveWizards: machine learning company.
Main projects in which I participated: 
- Face recognition and person identification from real-time streaming cameras. 
Technology stack: Python, TensorFlow (FaceNet NN architecture), Apache Kafka, Cassandra, Lua, PostgreSQL, AWS API (S3, FR), Baidu API (BOS, FR), Docker. 
- Detection of abnormal behaviour of hospital employees (doctors, nurses, etc.) based on workstations log activity, for instance, VIP snooping, looking of disease history of colleagues and so on. 
Technology stack: Python, Apache Spark (working with parquet files), scikit-learn, plotly, Google Geolocation API. 
- Identification of a Salary Board’s Job Index (SBJI) for a job title based on industries and levels hierarchy proposed by Salary Board. 
Technology stack: Python, Pandas, NumPy, NLTK, fuzzywuzzy, pattern. 
Other activity: 
- Development of web applications and RESTful API services 
- D3.js dashboards and visualization 
- Research 
- The education courses assistant writer and blog-posts writing (Python, ElasticSearch, MongoDB, Spark, CNTK)
Position: Machine Learning Engineer, Data Scientist, Research Engineer
Category: Data Science
Experience: 1 years | English: fluent

I have a PhD degree in statistical physics, which is a quantitative field closely related to data science.
I work as a research scientist/engineer with primary focus on statistical modeling and computer simulations of molecular systems. A large part of my work is devoted to data analysis, visualization and presentation. I have experience applying both classical and deep learning algorithms to exploratory data analysis, pattern recognition, image identification, NLP, as well as standard regression/classification supervised learning tasks.  

Due to strong mathematical background, I have thorough understanding of machine learning algorithms as well as artificial neural network (NN) architectures, including Fully Connected NN,  Convolutional NN, Recurrent NN (LSTM, GRU). My primary frameworks for machine learning tasks are Python Scikit-learn, Keras, and Spark MLlib. For data analysis and visualization I use Python NumPy, Pandas, Matplotlib, and SQL. I also have experience deploying and working with big data sets on AWS clusters (Elastic MapReduce, Deep Learning AMI, Apache Spark).
I have a passion for exploration and modeling of real life processes via analytical and data driven approaches. 
Due to this and other reasons, in my future career I intend to focus on the fields related to artificial intelligence (data science, machine learning, research).

At a new job, I would love to learn something new and grow as a professional. I enjoy working both independently and as part of a team due to extensive experience of collaboration with other research groups.
Position: Machine Learning Engineer , Data Scientist (strong junior, middle)
Category: Data Science
Experience: 1 years | English: upper

2nd place at "Hack4Retail" hackathon, Data Science for Retail (November 2021)
2nd place at "IPT Hack", Data Science (February 2022)
Participation in Kaggle competitions.
I'm passionate data scientist with solid mathematical base and
different experience in Machine Learning.
Classic ML, CV and NLP projects. Completed educational ML courses on Coursera, Stepik. Participation in data science competitions (2-nd prize on "Hack4Retail", 2-nd prize on "IPT hack", Kaggle challenges).
Currently looking for "Data Scientist", "Machine Learning Engineer", "Computer Vision Engineer" roles.

My skills:
Applied Mathematics - Probability/Statistics/Stochastic processes, Optimization, Numerical methods;
Algorithms and data structures;
Python (numpy, scipy, pandas, matplotlib, sklearn, PyTorch, OpenCV, Blender);
C/C++ (standard library, SFML);
MySQL/SQL Server;
Classic machine learning (classification, regression tasks, clustering, reduction of dimensions, ranking, etc); 
Computer Vision (image classification, detection, segmentation, 3d reconstruction);
Natural language processing (classification, generation of text, topic modeling, sentiment analysis, etc).

More information in CV and Linkedin profiles
I like all kinds of Data Science, I have a lot of experience with tabular data. Computer vision is preferable, but also able to develop projects in the NLP direction.

Not interested - PowerBI/Tableau, Data Engineer roles.
Position: Machine Learning Engineer, Deep Learning Engineer
Category: Data Science
Experience: 5 years | English: fluent


Have experience in Reinforcement Learning, Big Data, Computer vision, classical Machine Learning, and NLP. 
Trained software engineer with good programming skills.
In my free time giving lectures and writing articles on Reinforcement Learning.

Have experience in leading a team of 4 people.

Projects I've worked on:
- AlphaZero based on ELF framework
- RL algorithms to build AI-bot for the game of Monopoly
- Locomotion Behaviours research
- MineRL competition
- CV system for weapon detection
- CV system for price tag recognition
and many more

Giving lectures on AI and RL at Grammarly, ARVI Lab and Lalafo companies.
Professional growth, improving knowledge of the field
Position: Machine Learning Engineer, Deep Learning Engineer, Computer Vision Machine Engineer
Category: Python
Experience: 0 years | English: upper


I led a team project where we developed a deep learning library in C++ with enhanced GPipe parallelism, incorporating CUDA support for GPU acceleration and ability to create non-sequential model. Our version of GPipe resulted in significant performance improvements, making the training process up to three times faster compared to original GPipie implementation. Furthermore, my personal project on implementing a stable diffusion model in TensorFlow with attention mechanisms demonstrated my skills in designing and implementing complex models using state-of-the-art techniques.
Position: Machine Learning Engineer, Deep Learning engineer, Data Scientist
Category: Data Science
Experience: 5 years | English: upper

Architecturing a conversational AI system. Project scope research, definition and estimation. The AI side of project planning. Data collection pipeline setup and development. Building, testing, deploying DL models as a service. Hypothesis testing, reporting for C-level stuff.
* Computer Vision, Natural Language Processing and Reinforcement Learning
* Building, deploying, maintaining ML and DL models in production environment
* Hypothesis testing, reports creation, experimentation logging, ETL processes
* Image processing, Biometrics processing, Named Entity Recognition, Topic modelling
* Language modelling, Text classification, Conversational AI, * Research and Development
* ML for Financial Applications (Portfolio optimisation, Trend analysis, Sales forecasting)
* Anomaly Detection, Time series analysis, Models debugging and explainability
Looking for breathtaking projects and challenges from the whole world in the Data Science or Deep Learning industry. Startups, product companies are welcome. Fintech and crypto projects also likely to be discovered
Position: Machine Learning Engineer, Deep Learning Engineer, Data Scientist
Category: Data Science
Experience: 2 years | English: upper

On my last position, I integrated Tensorflow.js as platform extension for machine learning, developed and owned a package for bioinformatics data analysis, as well as several other packages that help to analyze cheminformatics data.

CONTESTS
2019     All-Ukrainian Student Olympiad in the field of Computer Science, Final, Kharkiv.
2019    3-rd place, All-Ukrainian Student Olympiad in the field of Computer Science, Round 1, Vinnytsia.
2020    2-nd place, All-Ukrainian Student Olympiad in the field of Computer Science, Round 1, Vinnytsia.
2020    1-st place, All-Ukrainian Student Olympiad in the field of Computer Systems of Artificial Intelligence, Round 1, Vinnytsia.

CONFERENCES
2019     “A Conceptual Model of a City Road Traffic Research System” Databases: Development and Implementation Conference on Faculty of Physics and Technology Days 2019, Vinnytsia, Ukraine
I analyzed a timeseries and its correlation to other timeseries. Developed various bioinformatics and cheminformatics data analysis tools. Developed platform extension for machine learning with Tensorflow.js.
I expect to research, develop and train deep learning models; prepare the datasets for the models; deploy models in production.
Position: Machine learning engineer/ Deep learning engineer/ Research engineer
Category: Data Science
Experience: 3 years | English: intermediate

- Research and create ml models from idea to production
- Leading and creating research tasks for other engineers
- Management of the collected data and annotation process
- Do computer vision research in two companies for about 3 years. Works with TensorFlow, Keras, PyTorch for research models. Convert models to inference engines and participate in the deployment models process. 
- half-year - Team Leader small data science team.
I wanted to work in the Deep learning domain, research, and create SOTA models in computer vision or NLP
Position: Machine Learning Engineering
Category: Data Science
Experience: 4 years | English: upper

I have two papers about the performance (accuracy, robustness) of sparse neural networks one of my papers won first place at the TDK conference in Budapest under the title "Accuracy and robustness of sparse neural networks from Watts-stargaze prior"
Deep Learning Engineer – Intern (continental.com)
Speed up classifier runtime inference
The aim is to do R&D on the effect of using dynamic neural networks on the inference real-time for both
GPU/Hardware.
 - Did research about the available dynamic neural networks that could speed up the inference time of our
classifier.
 - Selected the promising DNN structure that matches our requirements.
 - Presented our goal, selected method, and plan to the team.
 - Generated and analyzed plots during the training and testing phases for various tuning parameters.
 - Speed up the classifier's inference time by 37.5% using dynamic neural networks (SkipNet), resulting in real-
- time performance on GPU/Hardware.
- Modified the forward function of the dynamic neural networks (SkipNet) according to the skipping mask.
- Investigated the improvement in the real product by compiling the network on the hardware and testing it.


3D Object Detection using Multi-Cameras
The aim of the project is to study the effects of using multi-cameras without lidars or Radars on 3D object
detection problems.
Compare the performance of the state-of-the-art methodology that used multi cameras in 3D object
detection.
-  constructing robust data pipelines of the Nuscenes dataset and others.
- Drive a camera-only method from the available sensor fusion methods.
- Transfer multi-camera views to birds eye view.


Machine Learning Engineer

Adaptive Chatbot to 100 Languages
The project aims to build a customizable chatbot platform where the user can build his own chatbot that fits
his business domain.

English Essays Correction
The aim of the project is to build an AI system that is able to auto-correct and score English essays.
Position: Machine Learning Engineering
Category: Python
Experience: 1 years | English: fluent


• Reviewed specialized images, videos, voice recordings, and other digital media to look for bugs and errors; sent report back to main team with list of errors found.
• Presented final report to head of ICV Laboratory and Executive Team focusing on process of solving errors, describing main system and special recommendations. 
• Researched APIs and how company can use process to make more efficient recommendations; looked at advantages and disadvantages associated with technology.
Position: Machine Learning Engineering / Data Scientist
Category: Data Science
Experience: 3 years | English: intermediate


I have more than 3 years of ML/DS.
Have more than 2 years of experience with AWS and can build architecture for ML features for products. Now working mostly with NLP-specific tasks.

Works on different projects:
- NLP supervised & unsupervised learning 
- Google Analytics
- Data mining from different sites.
- Real-time object detection and classification (OpenCV + dark flow and Yolo ann model) 
- Image classification (Keras)
- Object detection (RetinaNet on PyTorch and Tensorflow)
- Calculation of the project budget and prediction profit
- Social profiles categorization by interest area
- Data Matching
- Recommendation/Personalization system

AWS:
S3, SQS, Kinesis, Lambda, Redshift, DynamoDB,
ECR, ECS, Fargate, SNS, SageMaker, Personalize, Step
functions, AWS Elastic Beanstalk, EC2

Also have experience with full-cycle development: from investigation to final implementation and monitoring feature.
Position: Machine learning engineer (Middle)
Category: Data Science
Experience: 3 years | English: upper

I successfully integrated fully automated pipeline into a video processing (surgery recording). It contains clustering, depersonalisation, tools detection and tracking, phase recognition (AWS).
I trained and deployed lightweight text classifier (products taxonomy) into the marketplace (AWS Sagemaker)
Had experience deploying artefacts on Android.
Last 3 years I've been working with data as a Machine Learning engineer. This includes data engineering (~40% of the time), training the ML/DL models, deployment and other ML Ops. 
Data: images, videos and text. Apart from that, sometimes I use unsupervised (semi-supervised) learning for support. For example, out-of-distribution samples detection.
In the past, I had 5 years of experience as a software engineer. Worked with web backend (on premises and cloud), using PHP and Python.
While I am not actively seeking "prompt engineer" positions, I do have the expertise and capability to fulfill such roles and I am available for short-term contracts to provide assistance.
Position: Machine Learning engineer / MLOps
Category: Data Science
Experience: 5 years | English: pre

Successful finished Master Master's degree (Computer Science with specialization in Data Science) in Ukrainian Catholic University
Passed a lot of Google Cloud Data Courses
Passing Google Cloud Certified Professional Data Engineer (in the process)
Preparing for Certified Kubernetes Application Developer (CKAD) (in the process)
- NLP tasks: generating sports news using RNN and BERT, categorize text, NER, topic modeling, classify fake/not fake news
- Computer vision tasks: object detections,  Lucas-Kanade tracking, simple Augmented Reality
- MLOps tasks: data pipelines using AirFlow, Dataflow, Luigi, ML pipelines using Kubeflow, MLFlow, serving models using Kubeflow, Seldon. Work with Kubernetes
- Python: Flask/Celery/Selenium/Conda
Have a big experience in web development: python/PHP and OPs: developing and deploying microservices and modular projects (with API, message and events integration patterns)
Have experience in team leading (worked 2 years as Team Lead of 4 developers)
Please, no data engineering and python developer offers.
Position: Machine Learning Engineer | MLOps
Category: Data Science
Experience: 4 years | English: fluent

-
Machine Learning | Data Science | MLOps

Hi!
I am Machine Learning Engineer. I have experience with different domains in ML such as MLOps, CV and strong SQL knowledge. 
In my experience responsibilities include starting from business requirements to serve a model and deploy to servers.
I have experience in MLOps domain and instruments like -> MLFlow and Airflow.

Projects:
Object Detection and Counting
Time-Series Analysis of an Entertainment App
Churn Prediction
Probability of Default
-
Position: Machine Learning Engineer | MLOps
Category: Data Science
Experience: 2.5 years | English: intermediate

- Microsoft Certified: Azure Data Scientist Associate
- Winner of "Hackathon Expert (2022)". We have solved computer vision problem from Global Logic. As the result, we provide unsupervised segmentation of video of some technological process and build granulometry distribution histogram, based on the resulting segmentation mask.
- I have successfully completed many courses at Coursera in the field of Machine Learning
- Have graduated from Deep Learning Creator School from AI House (developing DL models for content generation)
- Have graduated from BigData School 4.0 from Kyivstar (big data, data pipelines, developing ML model)
Machine Learning Engineer (06.2021-present)
- have an experience in developing ML pipelines in cloud (Azure stack)
- develop traffic forecasting model using ML algorithms and Graph Neural Networks
- build antifraud and scoring models using ML-algorithms
- build a model to find the optimal location for store opening
- develop custom tools for process automation (custom automl, data quality monitoring)
- work with a huge amount of data (structured and unstructured)

Big Data Analyst (02.2020-06.2021)
- write Ad-hoc queries using Python, Hive, PySpark, and other big data tools and frameworks
- build look-alike models using ML-algorithms
- develop a system for monitoring data quality
- develop "silver" and "gold" data sources

Pet-projects:
- emotion-swap-project
The main idea of this project is to create web-application for swapping emotion on target image using emotion-vector. For developing I have used FOMM, Clip, StyleGAN and custom Emotion Recognition NN.
- bert-ner-ua
Goal of the project is to improve accuracy of NER in Ukrainian-language texts using SOTA approaches in NLP.
- Now, I'm developing my skills in MLOps and Data Engineering fields. It would help me to write efficient code and cover the full lifecycle of ML project. I want to apply these skills at my new job.
- Availability of Deep Learning projects or projects with a lot of different data would be a plus :)
- Especially I'm interesting in Computer Vision projects
Position: Machine Learning Engineer/MLOps
Category: Data Science
Experience: 5 years | English: upper


ML Engineer with software engineering and MLOps experience.
Experienced in building distributed, high-load microservice architectures, productionalizing ML pipelines, and e2e model training.
Built AI voice bots (TTS, STT, Conversational AI), optimization systems for e-commerce.

Mostly focused on prototyping and building architecture for ML-based solutions.
Passionate about Deep Learning and Generative AI
Position: Machine Learning engineer/MLops engineer
Category: Data Science
Experience: 4 years | English: upper


Experience
----------
Machine Learning Engineer
Company A
City A, Country A
October 2021 - Present
- Developed a fraud detection model and deployed it to AWS alongside with a dashboard for business use. The project boosted the amount of found fraud cases by 50%.
- Integrated Feast feature store into an ongoing data science project. Unified feature processing, inference and training pipelines.
- Automated deployment and updates (CI/CD) using GitHub Actions, Docker, AWS(CodeBuild, CodePipeline) increasing update delivery speed.

Machine Learning Engineer
Company B
City B, Country B
October 2021 - June 2021
- Enhanced the quality of models by refactoring feature engineering and model creation steps. Improved model accuracy by 10%.
- Refactored distributed data processing part of a project, optimized memory usage.
- Supported k8s deployments, monitored and optimized resource usage.
- Refactored Slack logs sending system, introduced webhooks mechanism instead of deprecated tokens
- Created several ETL processes using Spark and Kafka.

Data Scientist
Company C
City C, Country C
Dec. 2018 - Oct. 2020
- Took part in IFRS9 modeling and created a new methodology of modeling for various bank products.
- Successfully delivered a pilot project on GCP using BigQuery.
- Created and deployed time-series models. Maintained close communication with customers.
My expectations are:
- Machine Learning Engineer/ Data Scientist/Data Engineer roles
- Looking for part-time jobs, however, might discuss full-time opportunities.
Position: Machine Learning Engineer (NLP)
Category: Data Science
Experience: 5 years | English: upper


I'm excited to delve into innovative concepts in the AI field and hold the view that hands-on experience is the best way to acquire new skills. My background includes work on various NLP applications like semantic search, named entity recognition, text classification, and text generation. I've also developed products using Large Language Models, such as ChatGPT, LLaMA-2, and Langchain Tools, and have constructed multiple search systems using machine learning techniques.
Highly skilled in NLP and Large Language Models (LLMs), I am currently seeking part-time opportunities (10-15 hours per week) to contribute to projects leveraging these technologies. With a strong background in AI and machine learning, I also offer consultancy services to businesses looking to integrate artificial intelligence into their existing workflows or new initiatives. My focus is on delivering actionable insights and scalable solutions to drive business success.