Worked with teams on designing, developing, maintaining, and deploying software components and tools to productionize forecasting machine learning models. Integrating these models into the company infrastructure to ensure that the development process is efficient, scalable, and reproducible. This work involves:- Automating the publishing of validated configurations for machine learning pipelines across all environments.- Creating configurable and self-sufficient ML pipelines with data… Show more Worked with teams on designing, developing, maintaining, and deploying software components and tools to productionize forecasting machine learning models. Integrating these models into the company infrastructure to ensure that the development process is efficient, scalable, and reproducible. This work involves:- Automating the publishing of validated configurations for machine learning pipelines across all environments.- Creating configurable and self-sufficient ML pipelines with data pre-processing, validation, training, inference, and post-processing steps.- Designing DB schema and integrating with data pipeline.- Integrating ML pipelines into the company architecture and incorporating it with the other services, API and UI.- Monitoring, reporting, and alerting on the execution and results of ML pipelines using Slack alerts and Grafana dashboards based on Prometheus time-series data.• Led a team of 7 engineers and QAs, along with a PO and Agile Coach, following Agile processes and principles.• Overseed the end-to-end software development lifecycle, ensuring high-quality and timely delivery of AI/ML products.• Collaborated with other teams to implement features and reduce tech debt on jointly owned codebases.• Conducted design and code reviews, pairing, and one-on-ones.• Led technical stage interviews to recruit new team members.• Tech stack: Java, Python, Argo workflows, VertexAI, K8s, CI/CD, GCP, PostgreSQL, Git Show less

• Design, development, deployment, and maintenance of machine learning pipelines using Python and Java.• DB schema design and feature development for data pipeline.• Collaboration with other teams to implement features and reduce tech debt on jointly owned codebases.• Design and code reviews.• Tech stack: Java, Python, Argo workflows, VertexAI, K8s, CI/CD, GCP, PostgreSQL, Git

• Design, development, deployment, and maintenance of machine learning pipelines using Python and Java.• DB schema design and feature development for data pipeline.• Collaboration with other teams to implement features and reduce tech debt on jointly owned codebases.• Design and code reviews.• Tech stack: Java, Python, Argo workflows, VertexAI, K8s, CI/CD, GCP, PostgreSQL, Git

• Backend software development of various platforms including public REST APIs using Python and Java frameworks such as Flask and Spring Boot, Spring WebFlux.• Worked with relational databases including PostgreSQL, Microsoft SQL Server, MariaDB and not relational databases like MongoDB.• Hosted solutions on AWS, leveraging services such as EC2, SQS, Lambda, S3, and Kinesis.• Provided technical support for onboarding new clients to use the company’s public API.• Tech stack: Java,… Show more • Backend software development of various platforms including public REST APIs using Python and Java frameworks such as Flask and Spring Boot, Spring WebFlux.• Worked with relational databases including PostgreSQL, Microsoft SQL Server, MariaDB and not relational databases like MongoDB.• Hosted solutions on AWS, leveraging services such as EC2, SQS, Lambda, S3, and Kinesis.• Provided technical support for onboarding new clients to use the company’s public API.• Tech stack: Java, Python, Flask, Spring Boot, Spring WebFlux, K8s, PostgreSQL, Microsoft SQL Server, MariaDB, MongoDB, AWS, Git, Rest GraphQL APIs, Docker Show less

• Backend development of web applications across various platforms using Flask, Numpy, Pandas, Scipy (Python) with the integration of third-party components and services (e.g. AWS). • Worked with relational databases (MySQL) and NoSQL databases (MongoDB, Firebase).• Worked in a software development team using Git as the source control system.

Course: MSc Business Intelligence and Analytics Faculty of Science and Technology.The final project is Liquidation Risk Insight from Open Data Applying Hybrid Machine Learning Technique:Prediction of the three possible liquidation risk statuses for companies by applying hybrid machine learning techniques. • Data source: collections of XBRL and iXBRL files in Companies House repository.• Data retrieving strategy: developed methods in R with the usage “XBRL”… Show more Course: MSc Business Intelligence and Analytics Faculty of Science and Technology.The final project is Liquidation Risk Insight from Open Data Applying Hybrid Machine Learning Technique:Prediction of the three possible liquidation risk statuses for companies by applying hybrid machine learning techniques. • Data source: collections of XBRL and iXBRL files in Companies House repository.• Data retrieving strategy: developed methods in R with the usage “XBRL” library.• Feature selection techniques: linear discriminant analysis (LDA), ranking method (information gain), the chi-test.• Hybrid machine learning variations: сlustering+clustering, classification+classification and clustering+classification.• Applied algorithms: k-means, k-medoids, decision tree C4.5, Naïve Bayes, k-NN, SVM. The project was realised in RStudio with the usage MySQL for storing data. Show less

• Development and maintenance of accounting, financial and warehousing software.• Data transformation and migration across different storages. • Adjustment and automation of cost calculation process across several departments.• Gathering business requirements from the key stakeholders at the different levels.

• Development, support, integration and deployment of the accounting and financial software for the company. • Communication and collaboration with key stakeholders.• Data migration between various versions of accounting and financial software

• Gathering and analysing business requirements from clients across a wide variety of industries. • Development, support, and deployment of accounting and financial software.• Development and integration of ERP modules (accounting, finance, warehousing) for various organisations.

Implemented replication of reference data from centralised storage to local database on user’s workstations which allowed to minimise the start-up time of operator’s application by 75%.