ML/DL • CV/NLP • Production-Grade Intelligence Systems

Architecting and scaling AI systems from research to production. Building end-to-end intelligent platforms with production-grade reliability, scalability, and governance.

• Production MLOps Platforms — Deploy, monitor, govern ML models at enterprise scale
• Automation APIs — Browser automation + LLM integration for intelligent workflows
• Deep Learning Research — Computer Vision, NLP, advanced architectures
• System Architecture — Microservices, async workflows, Kubernetes orchestration
• Cybersecurity AI — Threat detection & network intelligence (incoming)

🤖 AI/ML Frameworks
   PyTorch • TensorFlow/Keras • scikit-learn • LangChain • Hugging Face

🧠 Deep Learning
   CNN • RNN • LSTM • DNN • Transfer Learning • Fine-tuning • Quantization

👁️ Computer Vision
   Object Detection • OCR • Image Classification • Embedding Models

📝 Natural Language Processing
   NER • Text Classification • Sequence Models • Bilingual Processing

🔧 Backend & Architecture
   Django • DRF • FastAPI • gRPC • Microservices • Async Patterns

🐳 Deployment & Infrastructure
   Docker • Kubernetes • Model Serving • FastAPI Runtimes • Jenkins • GitHub Actions

💾 Databases & Storage
   PostgreSQL • Redis • PL/SQL • Event-Driven Architectures

📊 Languages
   Python (Expert) • C++ • SQL • Bash

Open-source foundation for production ML teams

• Stack: Django/DRF • FastAPI • Celery • Kubernetes
• Key Features: Versioning • Approval workflows • Deployment management • Monitoring • Drift detection • Governance engine • Audit trails
• Architecture: Control layer • API layer • Runtime layer • Async layer • Orchestration layer
• Status: ✅ Production-ready | 📦 PyPI | Apache 2.0
• Repo: github.com/AyoubArdem/AI_Accelerator

Production-grade library for LinkedIn automation with LLM analytics

• Stack: Playwright • Google Gemini API • Gmail IMAP • DRF backend
• Features: Automatic 2FA handling • Profile collection • Lead intelligence • Batch processing
• Advanced: Context-aware prompt engineering • Verification code auto-extraction • Async-first design
• Status: ✅ Production-ready (active) | 📦 PyPI
• Repo: github.com/AyoubArdem/linkedin_autobot

PyTorch implementations spanning CV and NLP

• Includes: NER with Bilingual BiLSTM-CRF • Embedding models • Vision pipelines • OCR
• Focus: Production-aware best practices with educational clarity
• Repo: github.com/AyoubArdem/TORCH_AIHUB

Comprehensive collection of ML and deep learning projects

• Coverage: Classical ML • Deep Learning • NLP • CV • Object Detection • OCR
• Tech Stack: scikit-learn • TensorFlow/Keras • PyTorch • Pandas/NumPy • NLTK/spaCy
• Purpose: Reproducible examples, pedagogical clarity, end-to-end pipelines
• Repo: github.com/AyoubArdem/IA_PROJECTS

AI-driven threat detection & network intelligence platform

Distributed across Python ecosystem	Actively serving ML teams	9 distinct ML/DL architectures	MLOps • Automation CV • NLP • Security

Looking to build AI teams that move beyond research into production systems?

I bring full-stack fluency: production MLOps platforms, deep learning research, backend architecture, DevOps infrastructure. I architect systems where cutting-edge ML meets operational reality.

What I deliver:

• Complete ML lifecycle ownership (dev → deployment → monitoring → governance)
• System design expertise (async workflows, microservices, K8s, governance)
• Production discipline (versioning, documentation, backwards compatibility)
• 2+ published packages, 4+ open-source projects

I thrive in roles and environments that:

• Value cross-functional technical depth + execution rigor
• Own the full ML stack (not just model training)
• Balance innovation with operational excellence

→ Let's connect on LinkedIn or email me

Building something ambitious in AI/ML infrastructure or deep learning?

I collaborate on projects that push boundaries: production-grade systems with clean architecture, reproducible pipelines, thoughtful design. Work that outlives the initial use case.

Currently exploring:

• 🔧 MLOps coordination & intelligent governance workflows
• 🔗 Automation at scale with LLM integration
• 🧠 Advanced deep learning architectures & deployment optimization
• 🔐 AI security & network intelligence applications

→ Explore my repositories or reach out with ideas

Last updated: April 2026 | Open to opportunities in AI/ML infrastructure, deep learning, and production systems.