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Mykhailo Melnyk
ML engineer
Lviv, Ukraine | [open contact info](look above in the "contact info" section) | [open contact info](look above in the "contact info" section) | LinkedIn | GitHub

ML/AI Engineer with roots in academic machine learning: computer vision, predictive modeling – now applied to designing,
building, and deploying end-to-end ML systems across scientific computing, credit risk modeling, and LLM retrieval systems.
Currently focused on RAG architecture and evaluation pipelines. Open to ML/AI engineering roles.
Projects
Hierarchy-Guided RAG system
LangChain, LangGraph, Ragas, ZenML, MLflow, MongoDB, Qdrant June 2026 – present
● Designed a RAG system leveraging the notes’ natural hierarchical structure to guide retrieval strategy per query: low-
level chunks, high-level structure or bypassing retrieval entirely for general queries – cutting token spend and improving
context quality
● Split the pipelines according to FTI architecture (feature, inference, evaluation) with hash-based CDC for ETL and
feature pipelines, created extendable versioned configs as the single source of truth for different strategies. Used ZenML
for pipeline observability and MLflow for inference tracing and eval tracking
● Orchestrated retrieval with LangGraph, combining query rewriting, cross-encoder reranking, and chunk location
metadata to surface more precise, better-grounded context
● Designed a custom answer-correctness metric using LLM-as-a-judge for NLI entailment against a benchmark set of
expected atomic facts; achieved an increase in answer correctness from 0.45 baseline naïve recursive chunking to 0.73
using hierarchical method
Credit risk modelling system
Python, scikit-learn, optuna, FastAPI, Django, Docker Compose, LightGBM, XGBoost April – June 2026
● Built and deployed an end-to-end credit risk modelling system maximizing portfolio return through threshold-optimized
loan approval decision-making, achieving 1.73x aggregated return improvement on holdout set
● Deployed as two decoupled services – a FastAPI inference service and a Django web application with a database of
borrower information and loan decision tracking – containerized and orchestrated with Docker Compose
● Engineered feature-specific preprocessing across 151 raw variables, tailoring missingness, outlier, and encoding
strategies to each feature type and model family; found regression models to outperform classification formulation
Logo detection system
Python, OpenCV, Ultralytics, Roboflow, Albumentations, Google Colab May 2024
● Fine-tuned YOLOv8 on a custom Roboflow-annotated dataset with Albumentations augmentation using Google Colab,
achieved 0.991 precision and 0.983 recall; deployed locally for real-time video inference with interactive frame saving
Experience
Balloon path prediction application | Python, xarray, Qt, Leaflet.js, SQLite September 2024 – October 2025
● Owned the full lifecycle of a novel scientific computing application – requirements, architecture, algorithm design,
tooling, and end-to-end implementation – through independent research and iterative feedback with the commissioner
● Increased prediction accuracy by integrating multiple atmospheric data sources, flexible vertical path configuration and
model selection; ensured operational offline reliability via in-app forecast preload and priority-based failover system
● Implemented a full experimentation suite: launch and source parameters configuration, multi-path comparison on an
interactive map (including real flight paths), project saving and management
● Built a template-based parser for heterogeneous NetCDF/GRIB meteorological formats, enabling the system to ingest
new data sources without hardcoded format-specific logic
● Validated predictions against real balloon launches, presenting findings at SoftTech-2025
Skills & Certifications
LLM Engineering: LangChain, LangGraph, RAG pipeline design, Ragas, Qdrant
MLOps: ZenML, MLflow, Docker, Docker Compose
Development & Infrastructure: Python, SQL, JavaScript, FastAPI, Django, MongoDB, Git, Qt
Machine Learning & Data Science: scikit-learn, pandas, numpy, xarray, matplotlib, plotly, XGBoost, LightGBM, Optuna
Languages: English (professional working proficiency), Ukrainian (native)
Courses: Natural Language Processing with Classification and Vector Spaces (DeepLearning.AI), Financial Markets —
Robert Shiller, Yale University (Coursera)
Education
Kyiv Polytechnic Institute | Bachelor’s degree Kyiv, Ukraine | September 2021 – June 2025
Information systems software engineering
Relevant coursework: Neural Network Systems, Introduction to Data Science, Models and Methods of Artificial
Intelligence, Information Systems Infrastructure (AWS, Azure, VMware labs)