Denis Soloviov

Background

About Denis Soloviov

Senior Data Software Engineer & Machine Learning/MLOps Engineer with 5 years of experience, specializing in designing robust data pipelines, developing scalable machine learning models, and implementing MLOps practices to bridge the gap between data science and production. My approach integrates software engineering, DevOps, and MLOps best practices, resulting in efficient, user-friendly solutions that drive business impact.I'm dedicated to continuous learning and aligning technical solutions with business goals, fostering innovation and practical data systems. My passion for Machine Learning and MLOps has led me to design end-to-end ML pipelines, fine-tune models for specialized tasks, and deploy scalable solutions in production environments.I thrive in collaborative work environments, embracing knowledge sharing and teamwork as essential components for collective growth and success.Key Achievements- Implemented a Retrieval-Augmented Generation (RAG) chat-bot, empowering teachers with tailored lesson planning resources- Designed a framework for prompt configuration testing, enabling efficient evaluation of LLM-based systems- Fine-tuned an open-source transformer model for multi-label financial text classification, optimizing it for industry-specific insights- Developed end-to-end ML/LLMOps workflows, streamlining the journey from development to production for machine learning applications- Built an end-to-end data system for marketing analytics for one of the largest Ukrainian e-commerce companies- Designed and implemented a data pipeline for migrating from Google Analytics v3 (Universal Analytics) to Google Analytics 4- Improved data quality checks at critical points of the data pipeline- Enhanced the development and release process within the team- Led the migration from a legacy data system to a new one based on the Data Mesh approach.Proven Expertise In- Designing and building data and ML pipelines- Feature engineering and model fine-tuning for specialized tasks- Deploying scalable ML models in cloud environments- Implementing CI/CD pipelines for ML and data workflows- Building Retrieval-Augmented Generation (RAG) systems- Data modeling and data warehouse architecture- Conducting technical interviews for data-related roles