Building reliable, explainable AI systems with a strong engineering backbone.

Final-year Computer Science engineering student (Bac+5) specializing in Data Science & AI, graduating in 2026. I design and implement end‑to‑end intelligent systems: From data collection & ETL pipelines To model development (ML/DL, LLMs, RAG, agents) To deployment, monitoring, and MLOps in realistic environments. I have: 3 internships (AI/ML, Data Science, Full‑Stack) 10+ advanced AI projects (LLMs, RAG, document AI, vision, speech) 20+ GitHub repositories Multiple NVIDIA / Cloud / LLM engineering certifications Alongside AI, I have a solid full‑stack background: I’ve built web applications and APIs with Laravel, .NET, Spring Boot, React, Flask, Django, FastAPI, then containerized and automated them using Docker, CI/CD, and cloud services. I enjoy working where data, AI, software engineering, and cloud/DevOps meet, and I’m currently looking for a 6‑month AI/ML Engineering internship (from January 2026) in an international, product‑oriented environment.

• System thinking, not just models – I care about the whole lifecycle: data, modeling, deployment, monitoring, and feedback.
• LLM & RAG specialist – I build practical retrieval‑augmented assistants and AI agents around real products, not just notebooks.
• Strong engineering base – Experience with Laravel, .NET, Spring Boot, React, Django, Flask, FastAPI, clean APIs, and architecture.
• MLOps from day zero – Docker, MLflow, experiment tracking, versioning, CI/CD and reproducibility by default.
• Explainability and UX – From XAI to dashboards and clear documentation, I make AI understandable for non‑technical stakeholders.
• Continuous learner – NVIDIA DLI, DataCamp, 365 Data Science, cloud and DevOps courses to stay at the edge of AI practice.

“Powerful models matter. Robust engineering keeps them alive in production. Empathy makes them actually useful.”

role: AI & ML Engineer | Data Science | MLOps
background:
  degree: Computer Science Engineering (Bac+5)
  specialization: Data Science & AI
  graduation: 2026

strengths:
  - LLMs, RAG, AI Agents, NLP
  - End-to-end ML pipelines & MLOps
  - Document Intelligence & OCR
  - Computer Vision & Deep Learning
  - Time-series & classical ML
  - Full-stack web development (backend + frontend)
  - Cloud & DevOps fundamentals

web_engineering:
  backend: [FastAPI, Flask, Django, Laravel, .NET, Spring Boot]
  frontend: [React, Blade, Tailwind, Bootstrap]
  api_style: [REST, JSON, JWT-based auth]

relocation: Open to international internships
languages:
  - Arabic: native
  - French: native
  - English: professional
  - German: in progress

• ML/DL: PyTorch, TensorFlow/Keras, scikit‑learn, XGBoost, classical ML (SVM, RF, logistic regression)
• NLP & LLMs: Transformers, Hugging Face, Mistral, LLaMA, OpenAI API, tokenization, sequence models
• RAG & Agents: LangChain, LangGraph, FAISS, Pinecone, vector databases, retrieval pipelines, prompt engineering
• Data: Pandas, NumPy, statsmodels, feature engineering, time‑series forecasting

• MLOps: MLflow, experiment tracking, model registry basics, reproducible training pipelines
• Deployment: FastAPI, Flask, Streamlit, REST APIs, containerization with Docker
• DevOps / Cloud: GitHub Actions, Azure AI Fundamentals (AI‑900), cloud basics (compute, storage, networking), 12‑Factor App principles
• Ops mindset: logging, metrics, health checks, configuration via environment variables

• Relational & NoSQL: PostgreSQL, MongoDB, Redis
• Graphs & Vectors: Neo4j, FAISS, Pinecone
• Data ingestion: BeautifulSoup, Selenium, Playwright, ETL workflows for analytics and ML

• Backend: Laravel (PHP), ASP.NET, Spring Boot (Java), Django, Flask, FastAPI
• Frontend: React, Blade templates, Tailwind CSS, Bootstrap
• Architecture: RESTful APIs, authentication/authorization, RBAC, modular monoliths and API‑driven design

Jul 2025 – Oct 2025 · Remote

Project: Student Behavior Analytics & AI Recommendation System

• Analyzed large‑scale gameplay‑based learning data (timestamps, errors, response times) to derive learning behavior patterns.
• Built a hybrid detection pipeline mixing rule‑based logic with LSTMs / Transformer models to capture patterns and anomalies.
• Contributed to the learner profiling module, aggregating behavior signals into interpretable profiles.
• Helped design a RAG‑based recommendation engine (LangChain + vector DB) to propose evidence‑based learning interventions.
• Integrated explainability (XAI): confidence scores, semantic references, interpretable rationale for educators.

Jun 2025 – Aug 2025 · Hybrid

Project: AfriOffres — Pan‑African Public Tender Intelligence Platform

• Developed scalable web scraping (BeautifulSoup, Selenium/Playwright) to collect tenders from official portals.
• Designed and implemented MongoDB schema + FastAPI backend with advanced filtering and search.
• Built a personalized recommendation system (TF‑IDF, embeddings, collaborative filtering) improving Recall@10 by ≈15%.
• Implemented a RAG‑based AI assistant for tender analysis and proposal support.
• Added multi‑channel notifications (email, WhatsApp, dashboards) and a chatbot/e‑learning module for user guidance.

Jun 2024 – Jul 2024

Project: Eventify — Event Management Platform

• Built and maintained Eventify (Laravel + Tailwind CSS) for event creation, participation, and management.
• Implemented authentication, role‑based access control (RBAC) and optimized database operations via Eloquent ORM.
• Added real‑time community features (comments, interactions) using Laravel broadcasting and WebSockets.
• Integrated localization and caching to optimize event browsing performance.

Repository: Eventify Events Management App

• Azure AI Fundamentals (AI‑900) – Microsoft
  
  _{Cloud AI services, responsible AI, and deployment patterns.}

• Introduction to MLflow – DataCamp
  
  _{Experiment tracking, model registry basics, reproducible ML pipelines.}

• Understanding Cloud Computing – DataCamp
  
  _{Cloud foundations: compute, storage, networking, security.}

• 12 Factor App • Docker Training • Fundamentals of DevOps – KodeKloud
  
  _{Modern application design, containerization, CI/CD and DevOps culture.}

• Scrum Fundamentals Certified (SFC™) – ScrumStudy
  
  _{Agile/Scrum practices for iterative delivery and team collaboration.}

• 🎧 I enjoy podcasts and talks about AI practice, software architecture, and engineering culture.
• 📚 I like reading about psychology and learning science, which helps when designing educational AI systems.
• ☕ I believe a good coffee + a clean notebook solves most system design questions.

The best way to see how I think and work is to explore my portfolio and talk directly to my AI assistant:

• 🌐 Portfolio: aya-mekni-portfolio.vercel.app
• 🤖 Built‑in AI assistant: ask it about my projects, stack, or experience — it’s powered by RAG + LLMs on top of my real work.

If you want to know “Which projects best match this internship?” — you can literally ask the assistant.

• Email: [email protected]
• LinkedIn: aya-mekni
• GitHub: @ayamekni
• Phone: +216 92 819 319

“AI turns data into possibilities. Good engineering turns those possibilities into reliable products.”

AI & ML engineer with a strong software and cloud foundation, focused on building systems that are not only intelligent, but also usable, reliable, and deployable.