Ramal Mirzayev

Experienced AI Engineer and Data Scientist with a robust background in developing and implementing advanced machine learning models and AI-driven solutions. Proven track record in leading multiple projects, including the development of intelligent trading bots, autonomous drone systems, and innovative reinforcement learning applications. Demonstrated expertise in fine-tuning models, collecting and curating large datasets, and integrating AI technologies into diverse platforms. Strong collaborative skills with a history of working with cross-functional teams to deliver high-impact results. Passionate about leveraging AI to solve complex problems and drive technological advancements.

Project 1: Multi-Modal Medical Diagnosis Assistant

Developed an advanced medical diagnosis system combining medical imaging and patient data analysis. Implemented custom CNN architecture for medical image processing (X-rays, MRIs) achieving 94% accuracy in preliminary diagnosis. Integrated NLP models to analyze patient records and symptoms, creating a comprehensive diagnosis support tool. Utilized transfer learning with state-of-the-art vision models and fine-tuned LLMs for medical context. Deployed system serves 200+ queries daily with sub-second response time.

Key Technologies: PyTorch, MONAI, Transformers, FastAPI, Docker, Redis 

Results: 94% diagnostic accuracy, 40% reduction in diagnosis time

Project 2: Real-Time Traffic Analysis System

Built end-to-end traffic monitoring system processing live video feeds from multiple sources. Implemented YOLOv8 for real-time vehicle detection and custom tracking algorithm for traffic flow analysis. Developed time series models for congestion prediction with 87% accuracy. System processes 30+ video streams concurrently, providing real-time analytics and predictive insights.

Key Technologies: YOLOv8, OpenCV, Kafka, TensorRT 

Results: 87% prediction accuracy, 30+ concurrent video streams processed

Project 3: Advanced Customer Behavior Analytics Platform

Designed and implemented comprehensive customer analytics platform integrating multiple data sources. Built custom recommendation engine using hybrid collaborative filtering and deep learning approaches. Developed real-time anomaly detection system for fraud prevention. System handles 1M+ daily user interactions, providing personalized recommendations with 92% relevancy score.

Key Technologies: PySpark, XGBoost, Kafka, Elasticsearch, MLflow 

Results: 92% recommendation relevancy, 1M+ daily user interactions processed

Project 4: Call Analytics Solution
Developed an advanced call analytics systemthat leverages speech recognition, large language models (LLMs), and statistical techniques to analyze customer service and call center conversations. The system processes audio data in real-time, transcribes speech using faster-whisper and nemo_toolkit, and applies sophisticated analytics to extract actionable insights. Key features include sentiment analysis, topic detection, conflict detection, profanity word detection, and call summarization.

Key Technologies:nemo_toolkit, faster-whisper, transformers, torch, pyannote.audio, pydub, librosa, soundfile, scipy, openai, noisereduce, deepmultilingualpunctuation, ctc-forced-aligner, MPSENet

Results: Improved call center efficiency, reduced conflict escalation by 25%, and optimized overall customer satisfaction.

Project 5: Advanced Video Dubbing System
Designed and developed a comprehensive video dubbing solution for multilingual content creation. The system transcribes audio from video files using STT models, translates the text into target languages, and synthesizes speech using TTS models with cloned voices matching the original speaker’s tone and style. Integrated Wav2Lip technology for accurate lip synchronization, ensuring natural alignment between video and dubbed audio. The solution automates the end-to-end dubbing workflow, significantly reducing manual effort.

Key Technologies: TensorFlow, PyTorch, Wav2Lip, Pydub, FFmpeg, Mediapipe, Accelerate, gdown
Results: Delivered seamless multilingual dubbing with high-quality lip sync, reducing manual dubbing efforts by 70%.

Project 6: Advanced Futures Trading Bot for Cryptocurrency Markets
Designed and implemented an advanced futures trading bot for cryptocurrency markets on Binance Futures. The bot processes real-time market data and utilizes classic technical indicators (MACD, RSI, Bollinger Bands, and ATR) combined with candlestick pattern recognition (Hammer, Engulfing) to generate accurate trading signals. It automates trade execution for both LONG and SHORT positions and incorporates dynamic risk management strategies based on volatility, including ATR-based stop-loss, take-profit, and trailing stop mechanisms.
The bot is capable of multi-timeframe analysis, allowing confirmation signals across different intervals to improve trading reliability. Extensive backtesting was performed on historical data to validate the bot’s performance, ensuring robust operation in live trading environments with advanced error handling and retry mechanisms for API communication.

Key Technologies: Python, Binance Futures API, TA-Lib, Pandas, NumPy, Logging, Matplotlib

Results: Improved trading efficiency by automating complex strategies and reducing manual intervention in high-volatility markets, delivering consistent and reliable performance.

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