Technologies & Libraries

• Cloudflare for SSL, DDoS protection, and CDN

• Nginx as reverse proxy and static file server

• Gunicorn for Python WSGI app deployment

• PostgreSQL with PgBouncer for DB optimization

• systemd for service control and zero-downtime deployment

• Ubuntu Server in production; shared DB for dev on Windows

Key Implementations & Functionalities

• Multi-layer stack: Cloudflare → Nginx → Gunicorn → PostgreSQL

• High efficiency: PgBouncer manages 1000 Django → 12 DB connections

• Secure static content delivery with headers and gzip

• 9 domains hosted under a unified stack

• Handles over 100M media files with optimized caching

• Local development shares production database seamlessly

Technologies & Libraries

• Django 5.2.3 with built-in ORM and admin

• django-parler for multilingual support (20 languages)

• Django Q for async tasks without Redis

• PostgreSQL FTS with GIN indexes for search

• django-cms and django-grappelli for content and UI admin

Key Implementations & Functionalities

• i18n-ready models for Artists, Artworks, Museums, Articles

• SEO-friendly multilingual URLs auto-detect language

• Caching system using DB cache

• 20 millions indexed records for fast multilingual search

• Background task queue with admin monitoring

• Optimized queries for large-scale read performance

Technologies & Libraries

• LangChain and OpenAI/Gemini for content generation

• json_repair for structured output recovery

• Custom prompt engineering for content field accuracy

• Multi-source enrichment (Wikipedia, DB, etc.)

Key Implementations & Functionalities

• 4 models updated via AI: Artist, Artwork, Museum, Article

• 20 million records updated in 20 languages

• Batch scripts manage content regeneration workflows

• Fallback and retries ensure process continuity

• Logs, cost tracking, and validation post-processing

• Generates field-level computed metadata (e.g., tags, styles)

Technologies & Libraries

• Django templates for structured articles

• AI-assisted content synthesis and SEO tuning

• Image logic for selecting article visuals

• Scheduled generation via Django Q

Key Implementations & Functionalities

• Menu-based and "Top 25" articles auto-generated per category

• Links artworks, artists, museums into cohesive narratives

• Multi-language generation in 20 languages

• Uses metadata for historical context and keyword optimization

• Handles bulk runs with queueing system

• Enforces uniqueness and readability in all content

Technologies & Libraries

• GPT for prompt generation

• PIL/Pillow for image resizing and conversion

• Support for multiple image APIs

• Django Q for batch image tasks

Key Implementations & Functionalities

• AI generates article cover images and artwork alternatives

• User photos restyled into artistic formats (20+ styles)

• Automatic thumbnails in multiple resolutions

• Image optimization with WebP/JPEG and CDN delivery

• Storage auto-organized and cache-controlled

• Face detection and enhancement on portraits

Technologies & Libraries

• Django templates with Bootstrap 5 and HTMX

• JavaScript (ES6+), CSS Grid, and Flexbox for UI logic

Key Implementations & Functionalities

• Fully multilingual responsive interface with SEO optimization

• HTMX enables filter/search without full page reload

• Image delivery optimized for device and DPI

• OAuth-enabled auth flows, shopping cart, and payment integration

• Structured data and robots/sitemap management

• Full accessibility and performance optimization (PWA-ready)

Technologies & Libraries

• Languages & Core: Python, sqlite3 (Caching), pickle (Serialization), hashlib, re (Regular Expressions).

• AI Orchestration: LangChain (Chains, Retrievers, Prompt Engineering).

• LLM Providers: OpenAI API (GPT-3.5, GPT-4, GPT-4 Turbo), Replicate API (Llama 2-70b), Together AI (Llama 2-70b).

• Vector Search & Embeddings: ChromaDB (Vector Store), HuggingFace BGE Embeddings (BAAI/bge-large-en).

• External Search APIs: SerpAPI (Bing Search Engine integration), Wikipedia API (via WikipediaRetriever).

• Data Acquisition: BeautifulSoup4 (bs4) for HTML parsing/cleaning, requests for HTTP handling.

Key Implementations & Functionalities

• Hybrid RAG Architecture: Engineered a Retrieval-Augmented Generation system that dynamically aggregates context from three distinct layers: internal unstructured documentation (Vector Search), internal structured databases (SQL-like queries for Artworks/Orders), and external real-time web data (SerpAPI/Wikipedia).

• Multi-LLM Abstraction Layer: Developed a unified LLM_Engine class capable of seamlessly switching between proprietary (GPT-4) and open-source (Llama 2) models at runtime, optimizing for cost and performance based on task complexity.

• Intelligent Intent Analysis: Implemented a two-stage reasoning pipeline where an initial LLM call analyzes user input to generate a structured JSON plan, extracting specific search keywords and categorizing intent (e.g., "Visual_Arts", "Customer_Previous_Order") before executing retrieval tools.

• Context Window Optimization: Designed a specialized scraping pipeline that cleans, tokenizes, and truncates external web content to ensure relevant information fits effectively within the LLM's context window limits.

Technologies & Libraries:

• Python, Argos Translate (OpenNMT), Custom Parsing Algorithms, Standard Library (Global State Management).

Key Implementations & Functionalities:

• Enterprise-Scale Translation: Designed and deployed an automated system to translate 370,000 artworks into 9 languages (including French, German, Chinese, and Russian), significantly expanding international accessibility.

• HTML-Aware Parsing: Developed a character-stream parser to isolate and preserve HTML tags (e.g., <H1>, <A>) while selectively translating only the text content.

• Dynamic Resource Management: Built a system to automatically detect, download, and install missing neural language packages at runtime based on the required language pair.

• Performance Optimization: Implemented global in-memory caching to track installed language pairs, preventing redundant initialization and ensuring efficient high-volume processing.

Technologies & Libraries:

• Programming Language: Python

• AI & LLM Integration: OpenAI API (GPT-3.5/4), Replicate API (Mistral/Mixtral), Local LLM execution (ctransformers, AutoModelForCausalLM for Llama/Orca models).

• Frameworks: LangChain (Prompt management, caching), ChromaDB (Vector Database for RAG).

• Computer Vision: OpenCV (cv2) for image comparison (ORB feature matching), Multimodal LLM integration (LLaVA).

• Natural Language Processing: SpaCy (en_core_web_md) for semantic similarity and text parsing.

• Web & Networking: requests (Session management, Retries, Proxies), BeautifulSoup4 (HTML parsing), urllib, SerpApi (Bing Search engine integration).

• Data Management: SQLite (Custom caching implementation), Pandas, Pickle, JSON (demjson3, json_repair).

Key Implementations & Functionalities:

• Retrieval-Augmented Generation (RAG): Designed a custom pipeline that retrieves context from three sources before generation: internal WahooArt/ArtsDot databases (Lotus Domino .nsf endpoints), external web searches (Bing/Google via SerpApi), and Wikipedia (WikipediaRetriever).

• Multilingual Content Automation: Implemented a batch processing system to generate academic-style art articles across 6 languages (English, French, German, Italian, Spanish, Portuguese) targeting specific topics like Art Styles, Media, and Museums.

• Automated Publishing Pipeline: Created scripts to sanitize generated HTML, handle character encoding for European languages, and automatically POST content to the CMS (Content Management System).

• Data Scraping & Indexing: Built a system to scrape internal documentation urls, vectorize content using HuggingFace embeddings, and store them in ChromaDB for semantic search retrieval.

• Image Analysis: Integrated functions for comparing artwork images using varying computer vision techniques (OpenCV structural comparison vs. AI-based visual description).

Technologies & Libraries

Python 3.9 | Django 3.2 | MySQL | Nginx | Redis | Celery | Gunicorn
• REST APIs with Django REST Framework
• Secure deployments with HTTPS, RBAC, CSRF protection, and environment-based settings

Key Implementations & Functionalities

• Designed horizontally scalable server stack
• Integrated Celery + Redis for asynchronous ML job scheduling
• Built robust schema-migration and zero-downtime deployment pipeline
• Implemented rate-limiting, session/token authentication, and full audit logging
• Optimized database pooling and ACID transactions for concurrent ML workloads

Technologies & Libraries

Bootstrap 5 | jQuery | Chart.js | AJAX | Django Templates (Jinja2) | Django-allauth
• Responsive design with mobile-first approach
• Static asset optimization with compression and caching

Key Implementations & Functionalities

• Built real-time dashboards showing model metrics, accuracy curves, and system status
• Developed drag-and-drop data upload (CSV/Excel) with automatic schema validation
• Created dynamic prediction and billing interfaces with exportable reports
• Implemented dual-layer validation (client + server) for secure, seamless UX

Technologies & Libraries

Celery | Redis Pub/Sub | Python Multiprocessing | Celery Beat Scheduler
• Fault-tolerant distributed job execution
• Task persistence and monitoring

Key Implementations & Functionalities

• Built WorkDispatcher / Processor / Monitor trio for distributed ML job orchestration
• Enabled non-blocking training sessions (72 + hrs) with live progress updates
• Implemented priority queues, retry logic, and GPU/CPU resource management
• Added real-time performance metrics and dead-letter reprocessing

Technologies & Libraries

Pandas | NumPy | NLTK | Sentence-Transformers | Scikit-Learn Encoders
• Automatic data-type detection, encoding, and scaling
• Persistent encoder caching for consistent inference

Key Implementations & Functionalities

• Built bidirectional EncDec for consistent encode/decode transformations
• Developed unified pipeline for numeric, categorical, and text data
• Implemented semantic text embeddings and multi-modal feature fusion
• Optimized preprocessing (100× speedup via NumPy vectorization)
• Intelligent imputation, outlier detection, and JSON flattening for mixed data

Technologies & Libraries

TensorFlow 2.17 | PyTorch 2.8 | Keras API | CUDA | Scikit-Learn Metrics
• Model serialization (.h5 + metadata)
• GPU-accelerated training and hyperparameter optimization

Key Implementations & Functionalities

• Designed NNEngine for full lifecycle: architecture → training → evaluation → inference
• Auto-architecture search testing 50 + network variants via evolutionary algorithms
• Implemented early-stopping, learning-rate scheduling, and checkpointing
• Supported regression, multi-class, and multi-output prediction tasks
• Provided feature importance and confidence interval outputs for interpretability

Technologies & Libraries

Django ORM | Pandas | OpenPyXL | Django REST Framework
• Modular pipeline: DataReader → FeatureEngineering → NNEngine → API
• Persistent model versioning and cost tracking

Key Implementations & Functionalities

• Developed Machine orchestrator managing model creation, training, and prediction
• Exposed /learn, /predict, /status REST endpoints for external integration
• Reduced ML workflows to < 5 lines of code via MachineEasyAutoML API
• Added rollback and version control for A/B testing and reproducibility
• Supported batch inference (10K+ rows in < 10 s) and transfer learning