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I help startups, healthcare organizations, and growing businesses design and build scalable SaaS platforms, secure web applications, and AI-driven products that are reliable, compliant, and built for long-term growth. With experience across full-stack development, cloud architecture, microservices, and healthcare technology, I specialize in transforming ideas into production-ready software that serves thousands of users while maintaining high performance, security, and maintainability. Over the years, I have built and optimized SaaS products, healthcare platforms, enterprise applications, and API ecosystems using modern technologies such as .NET, FastAPI, TypeScript, Angular, React, Azure, AWS, PostgreSQL, and SQL Server. What I Can Help You Build ✅ SaaS Platforms * Multi-tenant SaaS applications * Subscription and billing systems * User management and role-based access control * Admin dashboards and analytics * API-first architectures ✅ AI-Powered Applications * AI assistants and chatbots * Document processing and knowledge retrieval systems * Automated workflows and business process automation * LLM integrations and AI-driven user experiences ✅ Full-Stack Product Development * Modern web applications using React, Angular, and TypeScript * REST APIs and backend systems with .NET and FastAPI * Cloud-native architectures on Azure and AWS * Database design, optimization, and caching strategies ✅ Healthcare Technology * HIPAA-compliant applications * EHR/EMR integrations (FHIR & HL7) * Telehealth and remote patient monitoring solutions * Secure patient and practitioner portals * Healthcare data interoperability Why Clients Work With Me 🚀 Product-Focused Engineering I don't just write code—I help shape scalable products, identify technical risks early, and make architecture decisions that support business growth. 🔒 Security & Compliance by Design Whether you're building a SaaS platform or a healthcare application, security, reliability, and compliance are considered from day one. ⚡ Performance & Scalability I have improved API performance by up to 5x through architectural improvements, caching strategies, and database optimization. 🤝 Clear Communication & Ownership I work closely with founders, product teams, and stakeholders to ensure projects stay aligned with business goals and are delivered with transparency. If you're looking for a senior engineer who can architect, build, and scale your SaaS product—from MVP to enterprise-grade platform—I would be happy to discuss your project.

Production AI systems that don't hallucinate, don't burn tokens, and don't break under load — built by an AI Engineer with 15+ years shipping RAG pipelines, LLM agents, and agentic workflows for SaaS and enterprise teams. Most AI contractors deliver demos. I deliver production systems. Before you commit, I'll run a live demo on your actual data — a working RAG pipeline, a trend-intelligence agent, or a Reddit analytics system — so you see real output before a single dollar changes hands. My stack goes from LangChain, LlamaIndex, and LangGraph all the way to custom LLM Ops, cost-optimized inference, and security layers (RBAC, guardrails, compliance). I've shipped multi-LLM agent systems combining Claude, Gemini, GPT, and open-source models — and I own the architecture end to end. Here's what I build: ✅ RAG Systems — semantic + hybrid + BM25 retrieval, reranking, evaluation, and monitoring. Accurate answers, no hallucinations. ✅ LLM Agents & Agentic Workflows — autonomous pipelines that replace manual ops and remove burden from your team. ✅ Evaluations — labeled golden datasets, baseline benchmarking, and LLM-as-judge scoring. ✅ LLM Orchestration — GPT, Claude, Gemini, Qwen, Kimi, and open-source models, cost-optimized. ✅ AI Automations — custom Python pipelines that save measurable time. ✅ Backend & Infra — FastAPI, Node.js, pgvector, Pinecone, Weaviate, AWS, GCP. Request a demo before ordering: ✨ RAG demo — production retrieval pipeline with hybrid search, reranking, and monitoring ✨ Reddit analytics demo — topic clustering, sentiment, trend detection, influencer mapping ✨ TikTok trend-finder demo — real-time trending video discovery with embeddings and ranking logic "We thought we needed AI. What we actually needed was him. He rebuilt our entire approach — cut costs, improved accuracy, and delivered something our competitors still can't replicate." — Head of Growth, B2C SaaS Selected builds: Computer vision models powering a 1M+ download iOS app. Revenue forecasting platform for $50M+ ARR SaaS products. Enterprise-grade RAG knowledge platform with real-time data and security layers. TikTok trend-intelligence agent using LangGraph and multi-LLM NLP pipelines. Send me a message with your idea, your current bottleneck, or just the word 'AI' — I'll tell you exactly how to build it right, or why it hasn't been built right yet. I respond within a few hours.

I am Native Arabic speaker with strong English skills and solid experience in Gen AI Evaluation and Rating , audio tagging, Data collection ,transcription , Voice recording and data annotation. Skilled in following detailed guidelines and working with various labeling. I have 6 years of experience working as search engine evaluator, AI trainer with leading crowdsourcing platforms that support data labeling for various AI application scenarios like(UHRS - RWS -Appen - Remotasks - OneForma -Telus - Populii -Outlier - Amazon Mturk - Clickworker - Oneforma). I have been involved in projects related to - Gen AI Evaluation & Rating - Text Data Annotation - Image, Audio, and Video Annotation - Content Labelling and Classification - Content Relevance and Search - content localization - Voice acting & Recording - Transcription & segmentation - Natural Language Processing (Egyptian Arabic). I have been also involved in projects related to Rating or ranking assignments ask contributors to identify the best model responses out of two or more options until the content is high quality enough to be accepted in a review process.

I build production-grade AI agents and multi-agent systems that run reliably in production, not just in demos. If you need agents that reason, use tools, and execute multi-step tasks autonomously, that is my core focus. 10+ years in software engineering, now fully focused on agentic AI. I cover the full stack of modern agent engineering, from protocol-level coordination to deployed SaaS and mobile products. ─── AGENTIC AI & ORCHESTRATION ─── → Agent harness architecture: OpenHarness, OpenClaw, MiniClaw, Hermes Agent → Conversational agent pipelines: OpenJarvis → Multi-agent systems: LangChain, LangGraph, CrewAI, AutoGen → MCP (Model Context Protocol): server design, tool registration, context routing → A2A (Agent-to-Agent), ACP, and A2UG protocol implementations → Pi.dev agent runtime and SDK integration → Agentic patterns: ReAct, Plan-and-Execute, Reflection, Tool Use ─── LLM & RAG ─── → RAG pipelines: ChromaDB, Pinecone, pgvector → LLM integration: OpenAI, Anthropic Claude, Groq, Ollama, Mistral → Fine-tuning, prompt engineering, structured output design → Embedding pipelines and semantic retrieval ─── SAAS & AUTOMATION ─── → AI-native backends: FastAPI, Node.js, Python → Workflow automation: n8n with agent-triggered pipelines → Web and mobile delivery: React, React Native, Flutter I work with startups and product teams who need more than a chatbot, teams building real systems where multiple agents collaborate and get work done end to end. Message me with what you are trying to automate or build, and I will tell you straight whether agents are the right call and how I would approach it. I have 100% Job Success | Top Rated Agency | CTO, ArmorTech & HTML5Solutions

🚨 Most AI integrations work in demos. They break in production. I'm an AI Engineer who builds reliable AI systems. My specialty is the layer where most projects fail: agent orchestration that survives real edge cases, RAG that retrieves accurately under load, and models that are measured, not just demoed. And I go deeper than most: real ML - LLM fine-tuning (LoRA/PEFT, 8B→70B on A100/H100), evaluation harnesses, and MCP-based agent tooling. That means production AI that's grounded, observable, and reversible. Background: Contracted as Principal AI Product Architect for a UK SaaS company — architected the automation engine that saved clients $12K/month and replaced 2–3 FTEs of manual work. Top Rated on Upwork with 100% Job Success. ------------------------------------ ➤ WHAT I BUILD ------------------------------------ ✔ Agentic Systems & Multi-Agent Orchestration LangGraph · CrewAI · AutoGen · MCP | tool use, memory, state, human-in-the-loop → Deployed agents replacing 2–3 FTEs of repetitive operations ✔ Production RAG (Grounded, Not "PDF Chatbots") Pinecone · FAISS · Milvus · pgvector | hybrid retrieval, reranking, citations, eval gates, "I don't know" when sources are silent → Cut support tickets 40% for a SaaS client ✔ MCP & Claude Code Engineering MCP servers/integrations · Claude Code workflows · function calling · tool contracts → Agent tooling that's reliable and testable ✔ LLM Fine-Tuning & MLOps LoRA/PEFT · TRL · quantization · vLLM-style serving · eval harnesses · CI/CD → Domain LoRA on DeepSeek-R1-Distill-Llama, 8B→70B, packaged adapters ✔ AI Voice Agents Vapi · Retell · ElevenLabs · Twilio | inbound/outbound, booking, routing → Human-sounding agents wired to calendars + CRM ✔ n8n / Workflow Automation Multi-step logic · CRM · lead pipelines · AI-triggered flows → 25+ hrs/week saved per client · 35% lead conversion lift ------------------------------------ ➤ MY STACK ------------------------------------ - Agents: LangChain · LangGraph · CrewAI · AutoGen · MCP · Claude Code - Models: OpenAI · Anthropic Claude · DeepSeek · open-source LLMs - Fine-Tuning: LoRA/PEFT · TRL · bitsandbytes · quantization · eval harnesses - RAG: Pinecone · FAISS · Milvus · PostgreSQL + pgvector · hybrid + rerank - Voice: Vapi · Retell · ElevenLabs · Twilio · Whisper - Automation: n8n · Make · Zapier · Webhooks · REST APIs - Backend: Python · FastAPI · Node.js - Cloud/MLOps: AWS (Certified) · GCP · Docker · Kubernetes · CI/CD · AWS Bedrock ------------------------------------ ➤ BEST FIT FOR ------------------------------------ → SaaS founders embedding AI agents into their product → Teams needing grounded, cited RAG (legal/finance/regulated) → Companies needing MCP / Claude Code agent engineering → Businesses deploying AI voice agents for calls & bookings → Anyone who needs AI that survives real usage ------------------------------------ Not sure what to build first? Send me your workflow or use case - I'll map 2–3 high-impact AI wins for free, then you decide. I review architecture before writing a single line of code. If you want AI that works when users depend on it - click Invite and describe the problem

Computer Vision Engineer specializing in real-time detection systems across surveillance AI, manufacturing inspection, medical imaging, and smart city infrastructure — deployed to production. I build end-to-end: camera input, model training, TensorRT optimization, edge or cloud deployment, and backend integration. Not prototypes. Working systems. WHAT I HAVE SHIPPED: Multi-tenant AI surveillance platform with 8 real-time detection agents — intrusion, loitering, occupancy, vehicle counting, LPR, fire/smoke detection, entry after hours, and late checkout. YOLO11s + PaddleOCR on RunPod GPU, RTSP stream management via MediaMTX, FastAPI backend on DigitalOcean, CI/CD via GitHub Actions with a self-hosted runner. Supabase for auth and Postgres. Full production deployment. Spine X-ray segmentation system — 25-class vertebrae labeling (C1 through L5) with automated clinical metric extraction: disc height, segment angle, neutral listhesis, interspinous gap. Dual architecture: ResNet34 U-Net baseline and a custom ConvNeXt-Tiny encoder with CBAM attention trained on real X-rays and synthetic DRR data from CT volumes. 0.92 Dice on full-spine segmentation. Real-time defect detection on NVIDIA Jetson Xavier for a food and beverage production line. TensorRT FP16 inference, zero cloud dependency, PLC integration for automated pass/fail. Defect catch rate improved significantly over manual baseline. LiDAR-camera sensor fusion pipelines for smart city traffic monitoring — ROS, DeepStream, NVIDIA Jetson, production-deployed infrastructure. CORE CAPABILITIES: → Detection, segmentation, tracking: YOLO variants, SAM2, custom PyTorch → TensorRT optimization: FP16/INT8 quantization, ONNX export, engine serialization → Edge deployment: Jetson Nano, NX, Orin, Xavier — hardware bring-up to production → Medical imaging: U-Net architectures, multi-class segmentation, DICOM, clinical metrics → Surveillance AI: multi-agent detection, RTSP streams, LPR, event-based alerting → Industrial integration: FastAPI, MQTT, EMQX, Azure IoT Hub, AWS S3, Docker, Supabase INDUSTRIES: Manufacturing quality control — food and beverage, general production lines Surveillance and security — multi-camera platforms, license plate recognition, event detection Medical imaging — vertebral segmentation, clinical measurement pipelines Smart city and traffic — LiDAR-camera fusion, intrusion detection, infrastructure monitoring I communicate clearly, surface blockers early, and deliver systems that run reliably in your environment.