1. Personal Information
2. Summary
3. Languages
4. References
5. Skills
   • Core Skills
   • Additional Skills
6. Professional Experience
7. Education
8. Certifications
9. Publications

• Full Name: Irina Ryndova
• Address: Antalya, Turkey (Remote Only)
• Phone (WhatsApp, Telegram): +905342342398
• Email: ryndovaira@gmail.com
• LinkedIn: ryndova-irina
• GitHub: ryndovaira
• Kaggle: ryndova

Machine Learning Engineer with over 9 years of experience, combining a strong background in software engineering with expertise in AI-driven solutions. Skilled in all stages of machine learning workflows, including data preprocessing, model development, and deployment. Recent projects include retrieval-augmented generation (RAG) systems and applications in healthcare. Equally comfortable working independently or as part of a team, with a collaborative and methodical approach to problem-solving. AWS-certified and open to expanding expertise into new areas.

• English: B2+ (Upper Intermediate)
• Russian: Native

A PDF of the recommendation letter is available via Google Drive. Further details, including referee contact information, can be provided upon request as needed.

• Programming Languages: Python, SQL
• Databases: Relational (MySQL, SQLite), NoSQL (Mongo), Vector (Pinecone, LlamaIndex, Chroma)
• Libraries & Frameworks:
  • Core ML Libraries: NumPy, Pandas, Scikit-learn, PyTorch, Keras
  • NLP & Specialized Tools: Hugging Face, LangChain, FAISS
  • Visualization: Matplotlib, Seaborn, Plotly
  • Others: FastAPI, MMDetection, lm-evaluation-harness, Supervisely
• Models & APIs: OpenAI API (ChatGPT), LLaMA 2/3, Gemini
• Machine Learning Techniques:
  • Retrieval-Augmented Generation (RAG)
  • Traditional / Deep Machine Learning
  • Natural Language Processing (NLP) / Natural Language Understanding (NLU)
  • Exploratory Data Analysis (EDA)
  • Data Processing & Analysis
  • Model Tuning and Evaluation
  • Data Visualization
• Operating Systems: Linux, Windows
• Development Tools: Docker, Docker Compose, Git, GitHub, GitLab, JupyterLab, Supervisely
• Infrastructure & Platforms: AWS, Azure, Google Cloud, IBM Cloud Pak for Data, Cerebras
• Experiment Tracking & Automation: MLflow, CI/CD Pipelines (GitHub Actions), Automated Testing
• Soft Skills:
  • Approachable and supportive colleague
  • Collaborative and methodical in problem-solving
  • Adaptable to diverse roles and team dynamics
• Domain Knowledge: Electronic Healthcare Records (EHR)

• Programming Languages: C++, Java, R, SPARQL (AnzoGraph DB)
• Libraries & Frameworks: Cython, Qt, pyTelegramBotAPI
• Development Tools: Google Test, Mercurial, TeamCity

Company Name: Quantori LLC
Location: Remote
Dates of Employment: August 2023 – November 2024

Project: Development of a Chatbot System for Oncology Treatment Support

Technologies Used:

• Programming Languages: Python
• Libraries & Frameworks: Pandas, NumPy, Scikit-learn, Hugging Face Transformers, FAISS, Pinecone, LangChain, lm-evaluation-harness
• Machine Learning Models: OpenAI API (ChatGPT-4), LLaMA 2/3, RAG pipeline
• Development Tools: JupyterLab, Pytest, FastAPI, Git, Docker (Docker Compose), Streamlit
• Infrastructure & Platforms: Linux, IBM Cloud Pak for Data, Cerebras

Responsibilities:

• Processed and structured raw datasets, including clinical records, genomic data, pathology reports, and laboratory results. Collaborated with domain experts to clarify medical terminology and resolve data inconsistencies.
• Performed EDA and created visualizations to analyze trends and improve data quality.
• Designed and implemented an RAG pipeline integrating public (OpenAI API) and private (LLaMA) data sources to provide evidence-based treatment recommendations.
• Fine-tuned LLaMA models using Cerebras infrastructure for private HIPAA-compliant data and OpenAI API models for public data, ensuring tone, relevance, and clinical alignment.
• Deployed the chatbot on-premise using FastAPI and developed a basic interface with Streamlit for user interaction and testing.
• Stored vector embeddings with FAISS during prototyping and transitioned to Pinecone for scalable deployment.
• Configured CI/CD pipelines with GitHub Actions to automate deployment workflows.
• Conducted basic testing of chatbot responses, including tone, relevance, and functionality, refining outputs based on feedback.
• Translated high-level research ideas into actionable technical tasks. Prepared regular progress reports to track milestones and communicate findings effectively.
• Onboarded new team members and supported them during their initial project phases.

Company Name: Quantori LLC
Location: Remote
Dates of Employment: September 2022 – August 2023

Project: Classifying and Scoring Edema (condition caused by excess fluid in the lungs) on Chest X-Ray Images

Technologies Used:

• Programming Languages: Python
• Libraries & Frameworks: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, MMDetection, Supervisely
• Experiment Tracking: MLflow
• Development Tools: JupyterLab, Pytest, Git, GitHub
• Infrastructure & Platforms: AWS (S3, EC2), Linux

Responsibilities:

• Processed labeled datasets provided by domain experts, converting them into COCO format to enable compatibility with MMDetection workflows.
• Collaborated on configuring MLflow for shared experiment tracking and performance monitoring, supporting reproducibility across the team.
• Assisted in conducting experiments with MMDetection, helping to evaluate and refine models for classifying and scoring edema features.
• Designed visualizations to present results and analyze model outputs, providing insights into model behavior and debugging processes.
• Provided software development support for the project, contributing to the research team’s goal of publishing a peer-reviewed study in Radiology Advances.

Company Name: Quantori LLC
Location: Remote
Dates of Employment: September 2021 – August 2022

Project: Discovering Genetic Patterns in Autoimmune Disease Patients

Technologies Used:

• Programming Languages: Python, SQL
• Libraries & Frameworks: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, HDBSCAN, SHAP
• Development Tools: JupyterLab, Pytest, Git, GitHub
• Infrastructure & Platforms: AWS (Aurora, S3, SageMaker), Linux

Responsibilities:

• Collaborated with researchers to analyze datasets from UK Biobank, supporting their efforts to uncover trends and generate insights into autoimmune diseases.
• Conducted extensive exploratory data analysis (EDA) to identify potential patterns and validate hypotheses generated by researchers.
• Performed patient segmentation through clustering techniques to group individuals with shared biological characteristics, aiding researchers in exploring sub-populations of interest.
• Applied SHAP to investigate feature importance during clustering, enhancing the interpretability and reliability of segmentation results.
• Retrieved and analyzed longitudinal electronic healthcare records (EHR) from AWS Aurora databases to provide researchers with detailed data summaries.
• Automated experimental pipelines and implemented testing processes to ensure reliable workflows and consistent data quality.
• Studied autoimmune diseases, traditional treatments, and ICD-10/9 classifications to gain the necessary domain knowledge for clustering and data analysis tasks.
• Prepared detailed reports summarizing EDA and clustering results, enabling researchers to identify potential genetic patterns and areas for further study.
• Reviewed the work of junior engineers, provided guidance, and resolved technical challenges to maintain project momentum.

Company Name: Quantori LLC
Location: Remote
Dates of Employment: July 2021 – September 2021

Project: Organizing Bioinformatics Data and Project Resources

Technologies Used:

• Programming Languages: R
• Infrastructure & Platforms: AWS (S3, EC2), Linux

Responsibilities:

• Collaborated with the lead data engineer to structure raw bioinformatics data provided in diverse file formats, ensuring accessibility and usability for analysis.
• Organized and consolidated the client’s project resources, restructuring R scripts and related materials into a clear and centralized repository to enable effective collaboration.
• Reviewed and refined existing R scripts to clean and process data, ensuring compatibility with the project’s requirements.
• Worked with the client to retrieve missing files and resolve inconsistencies in the provided data and resources.

Company Name: LevelUp
Location: Remote (Russia)
Dates of Employment: May 2020 – July 2021

Technologies Used:

• Programming Languages: Python, SQL
• Libraries & Frameworks: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Keras, XGBoost, LightGBM, Imblearn
• Development Tools: JupyterLab, Pytest, Git, GitHub

Responsibilities:

• Designed and developed comprehensive teaching materials, including lecture slides, coding exercises, and projects tailored for beginner-level students in small groups (10-20 participants).
• Delivered interactive lessons on Python fundamentals, object-oriented programming, and core data science workflows.
• Mentored students in Python scripting, data preprocessing, and feature engineering techniques, ensuring a strong foundation in practical programming skills.
• Evaluated homework assignments and projects to track student progress, providing constructive feedback to support their learning.
• Taught machine learning concepts, including regression models, decision trees, and ensemble techniques, with real-world examples.
• Introduced students to hands-on data manipulation, exploratory data analysis (EDA), and creating end-to-end pipelines using Python libraries.
• Guided students in using JupyterLab for effective coding workflows and debugging tools like Pytest to ensure reproducibility.

Location: Remote
Dates of Employment: October 2020 – June 2021

Summary:
Worked independently on diverse data science projects, focusing on developing machine learning models, performing data analysis, and extracting insights from complex datasets.

Technologies Used:

• Programming Languages: Python
• Libraries & Frameworks: Pandas, Scikit-learn, NumPy, Matplotlib, Seaborn, XGBoost, LightGBM, Keras, BERT
• Development Tools: JupyterLab, Git, GitHub
• Version Control: Git

Contributions:

• Payment Behavior Analysis: Built machine learning models, such as decision trees, logistic regression, and gradient boosting, to analyze payment behavior and classify users into distinct customer groups.
• Customer Satisfaction Improvement: Conducted sentiment analysis on Google Play reviews using NLP techniques, including tokenization and text vectorization, to identify key improvement areas and enhance user satisfaction.
• Click Prediction Model: Developed a predictive model using algorithms like random forests and logistic regression to determine the likelihood of users clicking on ads in a web browser.
• Data Exploration and Visualization: Conducted exploratory data analysis (EDA) and created visualizations using Matplotlib and Seaborn to effectively communicate insights.
• NLP and Text Analytics: Applied NLP techniques, including BERT-based text classification, to extract insights from unstructured text data.

Company Name: MTS AI
Location: Saint-Petersburg, Russia (Remote) Dates of Employment: June 2020 – October 2020

Project: Sentiment Analysis and Data Clustering

Technologies Used:

• Programming Languages: Python 3
• Libraries & Frameworks: Pandas, Scikit-learn, XGBoost, LightGBM, Imblearn, NLTK, NumPy, Matplotlib, Seaborn, pyTelegramBotAPI
• Development Tools: JupyterLab, Pytest, Git, GitLab
• Operating Systems: Linux

Responsibilities:

• Clustered and classified data from customer reviews and conversations with bots and agents for sentiment analysis.
• Developed supplementary Python scripts and demo projects, including a Telegram Bot for data interaction.
• Performed data preprocessing, feature engineering, and model training using machine learning algorithms.
• Created unit tests using Pytest to ensure robustness and quality of data analysis pipelines.

Company Name: MTS AI
Location: Saint-Petersburg, Russia (Remote)
Dates of Employment: March 2019 – October 2020

Project: Development of an Automatic Speech Recognition Application

Technologies Used:

• Programming Languages: C++, Python 3, Cython
• Libraries & Frameworks: Kaldi
• Development Tools: Google Test, Pytest, Docker, Git, GitLab
• Operating Systems: Linux

Responsibilities:

• Created decoders using the Kaldi toolkit for automatic speech recognition (ASR).
• Led a small team of engineers, including task assignment, code review, and mentoring.
• Developed unit tests using Google Test and Pytest to ensure high-quality code.
• Set up and maintained pipelines for build preparation and testing.
• Developed Python function wrappers using Cython to integrate C++ functionality.

Dates of Employment: November 2017 – March 2019
Company Name: Speech Technology Center (STC Group)
Location: Saint-Petersburg, Russia

Project: Development of a Large-Scale C++ Project

Technologies Used:

• Programming Languages: C++, Python 3, Java
• Development Tools: Google Test, Pytest, SWIG 3, Docker
• Version Control & CI/CD: Git, GitLab, Mercurial, TeamCity
• Operating Systems: Linux, Windows

Responsibilities:

• Contributed to the development of a large-scale speech SDK project in C++ with over 10 years of active development.
• Created supplementary scripts in Python 3 to enhance project functionality.
• Took part in demo projects to showcase the product to clients.
• Developed a Java function wrapper using SWIG 3 for the C++ project.
• Managed build configurations in TeamCity for continuous integration.
• Prepared unit tests using Google Test to ensure code quality.
• Participated in pre-release integration testing with Java and Python.

Dates of Employment: March 2015 – November 2017
Company Name: Russian Institute of Radio Navigation and Time
Location: St. Petersburg, Russia

Project: Software Development and Legacy Code Migration

Technologies Used:

• Programming Languages: C, C++
• Libraries & Frameworks: Qt

Responsibilities:

• Supported and enhanced software solutions for navigation systems using C, C++, and Qt.
• Assisted in migrating legacy code from Assembler to C, improving software stability and maintainability.
• Worked on microcontroller programming for embedded system integration.
• Developed an FTP client-server application for secure data transfer over internal networks, meeting specific technical requirements.

Saint Petersburg State Electrotechnical University "LETI," Russia

• Master's in Computer Engineering and Informatics
  • Sep 2014 - Jun 2016
• Bachelor's in Computer Engineering and Informatics
  • Sep 2010 - Jun 2014

Credly Profile: irina-ryndova

• AWS Certified Machine Learning – Specialty
  Issued: Jan 2023 | Expires: Jan 2026

• IBM Data Science Specialization (Coursera)

• Applied Data Science Specialization (Coursera)

• Introduction to Data Science Specialization (Coursera)

• Stanford University - Machine Learning (Coursera)

• Other Courses on Coursera:

  • Python for Data Science and AI
  • Data Analysis with Python
  • Data Visualization with Python
  • Databases and SQL for Data Science

"Explainable AI to Identify Radiographic Features of Pulmonary Edema"

• Description: A study developing a deep learning method to identify radiographic features of pulmonary edema, a condition caused by excess fluid in the lungs.
• Contribution: Software development, validation, and manuscript review and editing.
• Published in: Radiology Advances, May 2024.
• DOI: 10.1093/radadv/umae003