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Trusted by 25+ organizations across 13 countries I help organizations build and govern AI they can stand behind. Whether you need someone to architect a compliant-by-design system or to lead the governance program that keeps it accountable, I work across both the technical and the regulatory side of AI so the two actually fit together. What I bring: - AI governance & management - Stand up and run AI management systems (ISO/IEC 42001), define policies and guardrails, map obligations across frameworks, and make AI auditable and defensible to regulators, clients, and boards. - Solution architecture & delivery - Design and ship LLM/NLP pipelines, RAG systems, and full-stack solutions for document intelligence, compliance, and decision support, from drafting board to production. - Privacy & risk by design - Embed privacy engineering (CIPT) and risk controls into systems from day one, not as an afterthought. - Program leadership - Run complex technical and governance initiatives end to end with disciplined project delivery (CAPM) and cross-functional leadership. I specialize in LegalTech, Compliance, and Regulatory Tech, and the same approach applies wherever AI meets high stakes and tight rules. My work is backed by award-winning, peer-reviewed AI research, so the systems and programs I deliver are grounded in current standards, not guesswork. Certifications - AIGP (Artificial Intelligence Governance Professional) - ISO/IEC 42001 Lead Implementer (AI Management Systems) - CIPT (Certified Information Privacy Technologist) - Azure Solution Architect (certified) - CAPM (Certified Associate in Project Management) Technical & Domain Skills - AI Governance & Privacy: ISO/IEC 42001 implementation, privacy-by-design (CIPT), regulatory mapping, ethical AI guardrails, compliance-focused system design. - System Architecture: Cloud solution architecture, secure backend systems, scalable AI services, APIs. - LLMs & Document Intelligence: Contracts, policies, regulatory text, semantic search, RAG systems. - NLP + ML/DL: Classification, summarization, extraction, embeddings, graph models. - Agents: Workflow agents, reasoning agents, domain-specific task agents. - Data Expertise: Large-scale text pipelines, knowledge graphs, structured/unstructured data. - MLOps & Deployment: Azure, Docker, continuous integration for AI models. - Project Management: CAPM-certified delivery, agile methodologies, cross-functional technical leadership. Adjacent Industries Applied AI in Finance, SpaceTech, HealthTech, InsurTech, M&A, Wealth Management, Automotive, Construction.

Senior Computer Vision Engineer specializing in production-ready AI systems. Over the past 5+ years, I have designed, trained, optimized, and deployed computer vision solutions across medical imaging, biotechnology, agriculture, remote sensing, and industrial applications. ✦ Core Specializations ─────────────────────────────────────────── ⚡ Object Detection & Real-Time Inference Custom detection pipelines built on YOLO, RT-DETR, RF-DETR & Detectron2. Single-modal and multi-modal. Optimized for speed, accuracy, and edge deployment — not just benchmark scores. ⚡ Medical & Biotech Image Analysis Classification, segmentation & prediction systems for clinical and research environments. Calibrated outputs, ordinal grading, custom loss functions designed for domain-specific constraints where off-the-shelf fails. ⚡ Multispectral & Aerial Computer Vision RGB, NIR, thermal — cross-modal fusion architectures for remote sensing, livestock monitoring & precision agriculture. Full pipeline ownership from raw sensor calibration to production-ready detection. ⚡ Video Intelligence & Motion Analysis Action recognition, temporal modeling & tracking from video. Real-time inference pipelines containerized and deployed on cloud GPU infrastructure. ✦ Technical Stack ─────────────────────────────────────────── Computer Vision: PyTorch · OpenCV · YOLO · RT-DETR · RF-DETR · Detectron2 · DINOv2 · Vision Transformers · SAM Image Processing: Camera Calibration · ChArUco · ArUco · Chessboard Calibration · Fisheye Calibration · Stereo Calibration · Image Undistortion · Image Rectification · Geometric Vision Machine Learning: Deep Learning · Classification · Segmentation · Tracking · Action Recognition · Pose Estimation · Multi-Modal Learning Deployment & MLOps: FastAPI · Docker · AWS · GCP · ONNX · TensorRT · CI/CD ✦ What to Expect ─────────────────────────────────────────── Requirements Discovery → Solution Design → Structured Milestones → Weekly Demos & Progress Updates → Production-Grade Delivery → Post-Deployment Support → Send a message with project details to get started.

Hi, I’m Hamdi, an AI Engineer focused on building practical machine learning and AI solutions for real-world problems. I help businesses turn AI ideas into working products, from ML models and LLM-powered tools to AI agents and full-stack applications. What I can help with: • Machine Learning & Deep Learning Building, training, and deploying ML/DL models for practical use cases. • LLM Applications Integrating large language models into apps, workflows, and automation tools. • AI Agents Creating simple agents with raw APIs, and more advanced agent systems using LangGraph and LangChain. • Full-Stack AI Products Building end-to-end AI solutions, including Next.js apps, databases, basic security, and deployment. I focus on creating AI systems that are useful, reliable, and easy to use, not just demos or experiments.

As an AI Engineer and Full-Stack Developer with 3+ years of hands-on experience, I build real-world AI systems and end-to-end applications that turn complex data into practical, production-ready solutions. I specialize in taking projects from model design to deployment, ensuring efficiency, scalability, and business impact. I have worked on AI-driven projects across document intelligence, semantic search, NLP, computer vision, and data analytics, delivering clean, well-documented solutions aligned with real business needs. My expertise includes: Machine Learning & Deep Learning: I design, train, and evaluate ML/DL models using Python (scikit-learn, TensorFlow, PyTorch, Keras), applying techniques such as classification, clustering, time-series forecasting, Siamese networks, GANs, and explainable AI on large-scale datasets. NLP, LLMs & RAG Systems: I build NLP pipelines and LLM-based solutions using Transformers, LangChain, and RAG architectures. My work includes semantic search, multilingual document processing, OCR + NLP pipelines, and intelligent assistants focused on delivering real business value. AI Backend & Full-Stack Development: I develop robust backend services with FastAPI, Flask, and Spring Boot, integrating AI models via REST APIs and connecting to SQL/NoSQL databases. On the frontend, I use React, Next.js, and Angular to deliver complete, user-friendly applications. Deployment & Data Engineering: I’m experienced with Docker, Git, Linux, CI/CD pipelines, and cloud platforms (AWS, Azure). I follow clean architecture principles to ensure solutions are maintainable, scalable, and ready for real-world use. I help turn AI concepts into reliable, fully integrated products that solve real business problems.

I'm in the Top 10% of AI engineers on Upwork with a 100% Job Success Score across all completed jobs. You're not looking for someone to wrap an API and call it an AI product. You're looking for someone who can architect a system that actually works in production and understand your business well enough to build the right thing, not just the obvious thing. That's what I do. I build production AI systems: multi-agent pipelines with LangGraph, RAG knowledge systems, LLM integrations with real memory and tool orchestration, and intelligent automation workflows. Not demos. Not prototypes. Systems that run, scale, and get maintained. What separates my work from most AI developers: I've spent years studying business strategy, systems design, and user psychology alongside the technical work. When I build for you, I'm asking "what outcome does this create for your business?" before I write a single line of code. I've worked across education, legal, e-commerce, and creative industries, and the through-line in every project is the same: I treat your business outcome as my success metric, not the sophistication of the code. Recent systems I've shipped: — Multi-agent LangGraph orchestration system for a real-time interactive application: 4 specialized agents with independent memory, shared world state, and supervisor routing. — Enterprise RAG pipeline handling 500+ documents with hybrid retrieval, source citation, and sub-2-second query response. — AI education platform deployed across 50+ teachers; dynamic persona architecture, curriculum RAG, and structured lesson generation. Active for 14 months post-delivery. — E-commerce AI support agent autonomously resolving 78% of customer inquiries with live Shopify and FedEx API integration. — AI marketing agent reducing campaign production from 3 days to 2 minutes, with performance prediction trained on 847 historical campaigns. Every engagement starts with a written scope document. You know exactly what you're getting before I write a line of code. Milestones are visible, progress is communicated proactively, and every delivery includes 30 days of post-launch support. If you want to pressure-test your idea before committing, book the 30-minute consultation. I'll tell you honestly what it would take, and whether there's a faster path to what you're actually trying to achieve.

AI/ML Engineer specializing in Data Science, Machine Learning, Deep Learning, Data Analytics. 🌍 Available across US, Canada, France, Australia, Middle East 📚 Publications: @Bechir Ben Tekfa, et al., DEXA 2025: Advanced Defect Detection in Industrial Imaging @Bechir Ben Tekfa, et al., KES 2025: Hybrid AI Systems for Renewable Energy Inspection ⏱️ 300+ billed hours delivering multi-domain AI & ML systems I design, implement, and deploy intelligent systems across multiple domains: computer vision, LLMs, time-series, audio, graph-based reasoning, and AI-driven automation. My focus is on real-world constraints, building robust, scalable, multi-modal systems that solve complex problems at once. 🧪 Core Expertise & Research Domains 1️⃣ Computer Vision & Visual Intelligence • Defect detection, anomaly localization, and automated inspection • Multi-modal inputs (EL, IR, RGB) with noise, bias, and real-world variability • Object detection, segmentation, hybrid CNN-attention architectures • Weakly supervised, long-tail, and low-data regimes • Multi-scale feature fusion, interpretability, and error analysis • Deployment-aware models for real-time, edge, and constrained devices 2️⃣ Machine Learning Foundations • Supervised, semi-supervised, self-supervised, representation learning • Time-series modeling, anomaly detection, forecasting • Graph-based learning, relational reasoning, and knowledge graphs • Mathematical rigor: optimization, regularization, uncertainty, generalization • Classical ML + deep learning hybrids for structured and unstructured data 3️⃣ LLMs & Reasoning Systems • Multi-step reasoning and orchestration pipelines • Retrieval-Augmented Generation (RAG) for domain-specific knowledge • AI agents for decision-making, planning, and automation • Reliability, hallucination control, evaluation, and system boundaries 4️⃣ Audio & Signal Processing • Speech-to-Text (STT) and Text-to-Speech (TTS) integration • Spectral, temporal, and multi-modal embeddings • Cross-modal pipelines (audio + vision + text) 5️⃣ AI-Driven Automation & Orchestration • Web scraping and data collection (Selenium, Scrapy, BeautifulSoup) • Workflow automation and orchestration (n8n, Twilio, Zapier-like pipelines) • Automated labeling, ETL workflows, ML pipeline orchestration • Continuous data ingestion and pre-processing for multi-domain AI systems • CI/CD, experiment tracking, reproducibility, and monitoring 6️⃣ Edge AI & Deployment Systems • NVIDIA Jetson and resource-constrained inference • Model optimization: pruning, quantization, TensorRT, ONNX • Dockerized deployments for reproducibility and scalability • Latency, memory, and energy trade-off analysis 🧰 Technical Stack AI / ML: Python, PyTorch, TensorFlow, scikit-learn, Hugging Face, OpenAI API Computer Vision & Signals: CNNs, Detection, Segmentation, Spectral Features LLMs & Automation: RAG, Agents, Orchestration, Evaluation Pipelines Data & Systems: Pandas, NumPy, SQL, PySpark, ETL pipelines Automation & Integration: Selenium, Scrapy, BeautifulSoup, n8n, Twilio Deployment & MLOps: Docker, ONNX, TensorRT, Jetson, CI/CD pipelines