I’m a software engineer and Python developer specializing in web scraping, automation, data science, software engineering, and machine learning/AI solutions. I help clients collect and structure data, build reliable backend systems, and develop ML/DL models that deliver measurable results. What I Can Do: 🟣 SOFTWARE ENGINEERING * Python (FastAPI, Flask, Django), plus other languages as needed * Backend development: REST/GraphQL APIs, authentication/authorization, role-based access * Database design & optimization: PostgreSQL/MySQL/SQLite, MongoDB; indexing, migrations * System design & scalable architecture: modular codebases, microservices, message queues (Celery/RabbitMQ) * Automation tools & scripting: ETL pipelines, cron jobs, background workers * Testing & quality: unit/integration tests (pytest), linting/formatting, CI-ready structure * Performance & reliability: caching (Redis), profiling, rate limiting, retries, graceful failure handling * DevOps & deployment: Docker, CI/CD, Nginx, Linux servers * Cloud deployment & maintenance: AWS/GCP/DigitalOcean, logging/monitoring, uptime-focused setups 🟡 WEB SCRAPING * Python with Scrapy, Selenium, Playwright, Seleniumbase, Pydoll, requests, curl_cff, ... * handling JavaScript-heavy sites * Anti-bot & CAPTCHA bypass * Data engineering (cleaning, transformation, storage) * Automation (scheduling, monitoring, error handling) * Data delivery (APIs, databases, CSV/JSON exports) * Cloud deployment (AWS, GCP, or DigitalOcean) * API development to access scraped data * High-Volume Data Collection: Scraping millions of pages for large-scale data sets. 🟢 MACHINE LEARNING / DEEP LEARNING / AI * End-to-end ML pipeline: data collection → preprocessing → training → evaluation → deployment * Classical ML: scikit-learn (classification, regression, clustering, anomaly detection) * Deep Learning: PyTorch / TensorFlow (CNNs, RNNs/LSTMs, Transformers) * NLP: text cleaning, embeddings, classification, sentiment, topic modeling, information extraction * Computer Vision: image classification, detection, OCR-related pipelines (when needed) * Time series & forecasting: feature engineering, backtesting, model comparison * Model deployment: serving via FastAPI/Flask, batch inference pipelines, Dockerized services * MLOps basics: experiment tracking, versioning, reproducibility, monitoring & retraining workflows * LLM/AI integration: prompt workflows, tool/function calling, RAG with vector databases (if required) 🔵 DATA ANALYSIS, DATA ENGINEERING & DATA VISUALIZATION * Data analysis: exploratory analysis (EDA), KPI definition, insights, trend/segment analysis * Data cleaning & preprocessing: missing values, outliers, normalization, deduplication, validation rules * Data engineering: ETL/ELT pipelines, batch/stream workflows, data modeling (star schema), warehousing basics