Egor T.

Developed proof-of-concept speech recognition system using MFCC feature extraction + DSP techniques (FFT, DCT-II, Mel filters) to reduce dimensionality and enable efficient ML model training. Key Responsibilities & Achievements: Clarified business needs w/ client/team; presented insights/research on DSP, speech math, SOTA architectures Built entropy-based VAD for noise-robust speech separation; framed signals w/ Hamming windowing FFT → Mel filter bank → DCT-II pipeline extracting decorrelated, perceptually-relevant MFCC coefficients Benchmarked SVM/RF/HMMs; trained 1D-CNNs on MFCCs; LSTM-RNNs modeling phonetic temporal dependencies Audio augmentation (pitch shift, time-stretch, Gaussian noise); hyperparameter optimization maximizing F1 across accents/noise Optimized Python pipeline (Librosa/NumPy); MLFlow experiment tracking + SageMaker training jobs Tech Stack: Python/NumPy/Pandas/Matplotlib, PyTorch/Hugging Face/Librosa, AWS (S3/EC2/SageMaker/ECR)Flask/PostgreSQL, MLflow

Developed AI software for ag robots covering pre-harvesting (RGB/NIR-based crops/weeds detection, disease spotting, field recognition) and harvesting (Sonar-driven fruit count/maturity estimation). Key Responsibilities & Achievements: Partnered closely with client to refine goals, addressing feasibility/end-use gaps for reliable, interpretable AI Built generic model quantization libraries and dataset staging pipelines—cut training time and eliminated duplication Benchmarked UNet, DeepLab, Mask R-CNN, ViT for RGB+NIR segmentation; automated pesticide/fertilizer application Adapted backbones for field classification, disease detection; implemented Multimodal YOLO/FusionNet fusion techniques Designed Sonar preprocessing + multi-output CNNs for fruit estimation; optimized models via OpenVINO for edge deployment Engineered real-time video pipelines for obstacle detection; added IG/XRAI interpretability layers Managed Linux robot filesystems; explored GAN/classic denoising; led code reviews

Built online food ordering platform featuring NLP-powered chat assistant for seamless user interactions. Key Responsibilities & Achievements: Conducted comprehensive EDA; designed scalable ETL pipelines Created custom semantic search matching user intent to menu items/ingredients/locations via NLP Fine-tuned BERT-like transformers for NER (items, quantities, dietary prefs) from chat requests Benchmarked recommendation architectures (collaborative filtering, matrix factorization, sequence models); deployed content-based system Developed NL → SQL query generation using code generation models Prototyped voice ordering w/ MFCC features + end-to-end speech recognition for accessibility Built FastAPI REST endpoints; Dockerized prediction service w/ AWS CI/CD (EC2/S3/SageMaker/ECR/EKS) Added SHAP interpretability + CloudWatch ML monitoring (drift, data quality, latency) Full documentation for team collaboration Tech Stack: Python/NumPy/Pandas, Scikit-learn/XGBoost/SHAP/SMOTE,TensorFlow/Keras