Ihar Nestsiarenia's home page
As an experienced machine learning engineer with over 8 years of industry experience, I possess a product mindset and a proven track record of successfully leading and delivering ML projects from R&D to production.
My expertise lies in NLP and related technologies, with a focus on transitioning models from research to production, building search systems, and improving relevancy. Additionally, I am skilled in quickly prototyping to validate business hypotheses.
Currently, I’m based in Lithuania, and considering relocating (priority: IT, UK, NL) and am open to consulting contracts in the MLOps industry.
My experience includes various roles such as team lead, machine learning engineer, delivery manager, product manager, and software engineer. I thrive in small, agile teams that can test ideas quickly, and am also well-versed in building systems and establishing production delivery.
Domains: NLP, Information Search, LegalTech, Document processing, OCR,
MLOps: AWS Sagemaker, DVC, MLFlow,
Core Languages: Python, Java
Summary: As a member of the EU product team, I was responsible for utilizing AI to address various challenges faced by legal professionals. This included designing and implementing search algorithms and other models to enhance legal research, as well as developing metrics, conducting experiments, establishing pipelines, and automating repetitive tasks.
Tensorflow, Keras, scikit-learn, spaCy, BM25, word2vec, Solr, DVC, Sagemaker, Python, Docker
Summary: As the Development Team Lead for an AI team, I played a crucial role in the design and implementation of document processing services for a case-management system for lawyers. The system included an enrichment pipeline that utilized a series of models to classify document types, extract named entities, recognize PDF document layouts, and extract key phrases. My responsibilities also included conducting R&D, prototyping, and implementing machine learning models and metrics to ensure the accuracy and performance of the system.
Team size:** ML team 3, project team 12 **
Summary: Worked on ML algorithms, ETL pipelines, model deployment, and result analysis for a legal tech product targeting EU markets. Collaborated with business stakeholders to implement ranking algorithms, language models, document segmentation, and sentence splitting for 4 languages. Created NLP models such as text classification, text segmentation, cross-lingual information retrieval, natural language query understanding, subject domain categorization, key-phrase extraction, and similarity detection.
Team size: 20+
Summary: During this period, I was involved in various AI-related projects and POCs, including work with deep learning, topic modeling, and document categorization, voice bot implementation, document classification, ranking algorithm implementation, and knowledge management. Additionally, I was responsible for deploying and integrating models in production. I collaborated with the business team in the early stages to identify areas where ML could bring value, and developed the ability to quickly prototype to validate business ideas.
Team size: 5
Tensorflow, Keras, scikit-learn, bigARTM, NLTK, spaCy, Fasttext, BM25, word2vec, gensim, Matplotlib, Jupyter Notebook, Node.js, Java, XSLT, and Elasticsearch
Summary: As senior engineer assisted in the migration of a backend research platform for lawyers, which included services, APIs, and a Content Delivery Channel. The platform was designed to perform batch processing of content and metadata and to deliver that content to the search engine for improved accessibility and searchability.
Team size: 8
Elasticsearch, graph databases, Semantic Web, RDF/OWL, SPARQL Jena-TDB, JBoss Fuse, Tomcat, Apache Fuseki, ELK, Kibana, Sonar, FindBugs, PMD, Checkstyle, Winscp, Bamboo CI, Git, JIRA, Maven, Ant, Linux, Cron, Java 7, Java EE, ActiveMQ, Spring 3, REST, SOAP, Tomcat, JBoss Fuse, Jena, XSLT, RDF, XML, XPath, Semantic Web, JUnit, Cucumber, Camel, Blueprint, Log4J, Lombok.
I was a primary instructor and contributor to the course “Fundamentals of Intelligent Data Analysis,” where I conducted lectures and exercises. The course covered various aspects of data mining, with a focus on natural language processing. It included fundamental theoretical knowledge about machine learning and practical-oriented exercises using modern NLP and ML tools.
Central topics discussed during the course were: fundamentals of machine learning; model performance evaluation, metrics, cross-validation; NLP basics, data cleaning, preprocessing, lemmatization, and stemming; developing the pipeline for text classification problem; neural networks for textual analysis.
Summary: As a co-founder, I played multiple roles including Product Manager, backend developer, architect, and DevOps, in the development of a product for small and near-to-middle retail businesses.
The product provided client base management with a loyalty system, accounting of purchases, and worker’s KPI monitoring. The project underwent several pivots, and ultimately resulted in the implementation of a CRM with personal analytics for small businesses.
Our solution is easy to integrate, allowing business owners to start using it within minutes and start tracking their sales, collecting client base and managing loyalty program for them. It offers detailed information about the client base and transaction history, simplifies communication with clients, and provides advanced analytics on demand. The product has a Monthly Active User (MAU) of 3,000.
Team size: 6
Flask, Reactjs, Kotlin, Swift, JUnit, Python, Vue.js, Linux, Docker, pytest, gitlab ci, Docker/docker-compose, git, maven, Gitlab CI, bash, trello, miro, notion, MongoDb, java 8, Spring Boot, Loki, grafana
Summary: The product was designed to solve several problems, including the recommendation of related products, which increased sales and enhanced client loyalty, and semantic search, which enabled search by symptoms and provided useful guides, as well as smart filtering for specific clients, such as pregnant women, lactating women, or children. The product also offered recommendations alternatives based on the official anthology of medicines. The project was partially acquired.
Team size: 8
Responsibilities: As a co-founder, my responsibilities included serving as a Product Manager, Technical Leader, where I organized the SDLC process, controlled code quality, configured CI/CD, and automated processes, Backend developer where I implemented the search system including fuzzy matching, ranking, and search by anthology graph, and developed a recommendation system based on rules, and finally, I was responsible for OPS, server configuration, proxies, and deployment management.
Technologies: Java 8, Spring MVC, Spring Security, Spring Data, Spring Boot, JSP, For admin-panel was used AngularJS. JPA/Hibernate, QueryDSL, Linux, Docker, JUnit / Spring Test Framework, docker-compose, git, Gradle, maven, Gitlab CI, Jenkins CI, bash, Python for data processing and aggregating from different sources, trello/gitlab issue tracker, Fiddler, MySQL (Used JPA/Hibernate, Spring Data)
As a course developer and trainer, I was responsible for creating and delivering a comprehensive Java programming course from scratch. The curriculum consisted of two sections: Basic Java and Java Enterprise Edition (Java EE). I achieved high student retention and more than half of the students successfully obtained employment in the IT industry.
As a co-founder, I was responsible for technical product management, DevOPS and backend development of an aggregation service for collecting advertisements for long-term rent. The service collected advertisements, deduplicated, and normalized them, and provided a search function with ranking, filtering, and sorting results. The project was closed as we were not able to find a suitable match between the product and the market.
The project included various components such as crawlers that ran on a schedule, a deduplication system that used fuzzy rules to detect the same advertisements from various sources, a RESTful API, a web application developed in React.js, and a server-rendering component based on PhantomJS that allowed us to create pre-rendered versions of the pages to help search crawlers index all pages of our app.
Team size: 2
Technologies: Java, Spring boot, React.js, PaaS OpenShift, maven, git, jsoup, mongodb, ODM morphia, docker, docker-compose, Linux, PhantomJS, Web Crawling, Search Engine Ranking, REST APIs, Product Management, DevOps
During my tenure, I contributed to core courses offered by the department. My responsibilities included:
I am a seasoned trainer and advisor with experience in a variety of disciplines including Java, Python, and Machine Learning. I have also been actively involved in mentoring activities, providing guidance and direction to individuals and teams.
Department: 05.13.05 Elements and devices of computer technology and control systems
Research domain: computer vision, object detection, vehicles tracking, traffic-light management, optimization
Summary: During my research, I focused on the integration of intelligent analytics and monitoring systems for road traffic.
My research centered on extracting information from video streams and developing optimization models to reduce traffic load. In a city where all roads and traffic lights are aware of road conditions in real time, the system could dynamically change traffic light regimes to normalize traffic flow.
I worked on optimization models that could be deployed on single-board computers. I developed a prototype for car tracking using OpenCV and deep neural networks, and presented my results at several international conferences, and published several papers on the topic.
Department: Mathematical modeling, numerical methods and program complexes
Diploma score: 10 (from 10)
Summary: As a continuation of my undergraduate studies, I conducted research on the modeling of dynamic transient processes using the Finite Element Method. My research resulted in the development of a modeling application that consisted of the following key components:
Department: Computer Engineering and Design
Diploma score: 5 (from 5)
Summary: I acquired knowledge of various approaches for designing web applications and gained proficiency in working with vector and raster graphics, including the basics of design. My final project involved the implementation of a 3D web editor using WebGL (Three.js) and Angular.js.
Department: Information Technology
Diploma score: 10 **(from 10)
Summary: During my student years, I gained research experience and participated in multiple conferences. I also presented a thesis related to my final project, which focused on the math modeling of transients processes. This project consisted of two parts: the application of the finite element method to the problem of heating a metal plate under a load, and the development of a software application with a visual editor, experiment management system, and custom math solver for solving problems.
### Project Initiation: Starting a Successful Project
### Foundations of Project Management
### XINE100 - 001 Introduction to Innovation and Entrepreneurship
### Digital Dolina
### School of management by Yandex (Мобилизация» 2017) (self-education)
### Scientific Thinking
### Introduction in Machine Learning (Yandex, SHE)
Coursera ( https://www.coursera.org/learn/vvedenie-mashinnoe-obuchenie/ )
### Developing Innovative Ideas for New Companies: The First Step in Entrepreneurship (University of Maryland)
### Machine Learning
### Getting and Cleaning Data (john hopkins university)
### M101P: MongoDB for Python Developers
MongoDB, Inc. (http://university.mongodb.com/course_completion/07078d93ef844636b2fd43d16eaccda7)
### M101J: MongoDB for Java Developers
MongoDB, Inc. (http://university.mongodb.com/course_completion/738aed1cec4a4f5bb980cf97dc79024b)
### edX Honor Code Certificate for Scalable Machine Learning with Spark
### Data Science foundations using R (john hopkins university)
### Линейная алгебра (Linear Algebra) (SHE)
### The Data Scientist’s Toolbox
### Programming Mobile Applications for Android Handheld Systems: Part 1 (University of Maryland)
### Scalable Microservices with Kubernetes
last update: March 1, 2023