AI / ML Engineer with Python backend skills (FastAPI) and experience in consulting.
I build ML pipelines, backend APIs, data products, and RAG-based retrieval systems.
Background in applied analytics (MSc) + hands-on engineering across data, ML, and backend. I ensure every product I build is technically solid, user-centric, and delivers real business value.

🔹 Munich / EU Work Permit / Remote-ready
🔹 ML Engineering • Data Science • Python Backend
🔹 English · German · Russian

• Machine Learning: feature engineering, model training, evaluation, deployment
• RAG: chunking, embeddings, retrieval tuning, hallucination mitigation
• Backend: FastAPI, Pydantic, PostgreSQL, MongoDB, REST APIs
• Data Engineering: preprocessing, pipelines, data validation, automation
• MLOps (intro): reproducibility, metadata, model/version control
• Consultations: data & tech maturity accessment, data strategy

Retrieval-Augmented Generation Service for the automated creation of final eNA contract texts. Integration via a dedicated /rag API endpoint into an existing backend. All sensitive content has been removed; only public or synthetic data is used. The repository "RAG_python_experiments" documents experiments on recursive chunking and a Markdown node parser for question answering.
Stack: LlamaIndex, OpenAI API (GPT-3.5 Turbo), Express.js
➡️ https://github.com/NuriaAk/Rag_contract_finder_and_experiments

A low-code matchmaking platform designed to connect start-ups with corporate partners in the circular economy. The system implements a structured intake, data normalization, and role-based access to enable scalable partnership discovery. Supports three distinct user roles: start-ups, corporate partners, and internal company departments, each with tailored data views and workflows.
Stack: Softr (frontend & role-based UI), Airtable (relational database & logic), Typeform (data ingestion & onboarding)
➡️ https://easymatch.circular-republic.org

REST API using Flask, managed dependencies with Pipenv, containerized with Docker.
Stack: Flask, Pipenv, Docker
➡️ https://github.com/NuriaAk/machine_learning_project/tree/main/05-deployment/flask_pipenv_docker_aws

Time-series & ML forecasting pipeline with feature engineering and model benchmarking.
Stack: pandas, scikit-learn, etna, prophet (for time series forecasting), FastAPI, Streamlit, pytest
➡️ https://github.com/NuriaAk/Car_accidents_forecast

FastAPI backend with CRUD operations and data models.
Stack: FastAPI, MongoDB, Pydantic
➡️ https://github.com/NuriaAk/FastAPI_Mongo_The_Lyric_Book

End-to-end ML pipeline + web UI for churn prediction.
Stack: pandas, scikit-learn, FastAPI, Streamlit
➡️ https://github.com/NuriaAk/Churn_prediction_streamlit_telco_all

ReDI School of Digital Integration

• Volunteer Teacher — Machine Learning, Intro to Python, Code & Data
• Mentor — student ML/analytics projects (model debugging, architecture, backend integration)

📧 nuriiaakbasheva@gmail.com
🔗 linkedin

🌍 Munich · Remote-ready