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Transforming complex AI challenges into scalable production solutions with 6+ years of proven expertise. I architect and deploy advanced machine learning systems that solve real business problems—from real-time computer vision applications to production-grade LLM implementations and enterprise MLOps pipelines. Core Expertise Large Language Models & NLP Built GPT architectures from scratch and implemented Retrieval-Augmented Generation (RAG) systems using Pinecone for enterprise knowledge retrieval. Specialized in fine-tuning LLMs, prompt engineering, and integrating external knowledge sources for enhanced model performance. Computer Vision & Deep Learning Delivered production-ready solutions using YOLO for real-time object detection, ResNet for image recognition, and advanced CNNs for facial recognition and semantic segmentation. Extensive experience with OpenCV, ImageNet models, and custom architectures optimized for edge deployment. MLOps & Production Engineering Automated end-to-end ML lifecycle management—from data pipelines and model training to deployment, monitoring, and continuous integration. Ensured reproducibility, scalability, and version control across production environments. Advanced Technical Stack TensorFlow - PyTorch - OpenCV - Pinecone - Transformers - Hugging Face - Docker - Kubernetes - MLflow - Weights & Biases - AWS SageMaker. Statistical & Programming Proficiency Python - R - C/C++ - MATLAB - Julia - SQL - Assembly - SageMath - SPSS - Minitab - JMP - JASP - CrystalBall - AMPL. Why Work With Me? Proven Track Record: Successfully delivered 50+ ML projects across computer vision, NLP, and predictive analytics with measurable business impact. End-to-End Delivery: From research and prototyping to production deployment and monitoring—I handle the complete ML pipeline. Clear Communication: I translate complex technical concepts into actionable insights aligned with your business objectives. Quality Assurance: Rigorous testing, documentation, and adherence to industry best practices ensure reliable, maintainable solutions. Recent Project Highlights • Developed custom RAG system reducing information retrieval time by 73% for enterprise knowledge base • Implemented YOLO-based real-time detection achieving 94% accuracy for manufacturing quality control • Built MLOps pipeline reducing model deployment time from weeks to hours • Engineered GPT-based chatbot handling 10K+ daily conversations with 89% user satisfaction Let's discuss how I can help solve your AI/ML challenges. Send me a message with your project requirements, and I'll respond within 24 hours with a tailored approach.

Since 2013, we’ve been building and deploying cutting-edge solutions at the intersection of artificial intelligence (AI), mixed reality (MR), and augmented reality (AR). Our products have supported clients in defense, healthcare, nuclear power, sports, and consumer tech—delivering real-world impact across industries. What We Do Best: 🔍 AI-Driven Computer Vision We design and deploy custom models for object detection, facial recognition, image segmentation, pose estimation, and depth sensing. Our team works with Apple’s Vision framework, YOLO, TensorFlow, CoreML, MediaPipe, and OpenCV—optimized for mobile, edge, and embedded systems. 🚀 OpenClaw ClawdBot Early Innovator! I created the first OpenClaw governance and safety layer (like antivirus but for controlling AI Agents) called ClawBoss.ai . It deterministically blocks unauthorized agent actions, secures users API keys, tokens, etc. and even anonymizes their personal information. 🧠 Machine Learning at the Edge From real-time inference on mobile devices to on-device learning for AR headsets and IoT sensors, we specialize in bringing intelligence to constrained environments—without reliance on the cloud. 🛠 Cross-Platform Spatial App Development Whether it’s ARKit on iOS, RealityKit on Vision Pro, or MRTK on Hololens, we bring ideas to life with a robust technical stack: Swift, Flutter, Unity 3D, Python, and more. We’ve built camera-based systems, gesture controls, UWB positioning features, and context-aware spatial workflows. 🧬 Human Behavior & Emotion Tracking We’ve built AI pipelines for facial expression classification, gaze detection, and skeletal tracking, applied in high-stakes domains like defense, public safety, and professional sports. 🚀 Rapid Prototyping & Productization We go beyond demos. Our team takes concepts from wireframes to working prototypes—and then all the way to deployed systems used in the field. Clients often tap us for technical validation, pilot programs, and IP-generating innovations. Let’s talk about how we can bring your next AI + AR project to life—efficiently, creatively, and with future-proof architecture.

Greetings, As a Senior AI Engineer, I design, build, and deploy production-grade AI systems — from predictive ML models to autonomous LLM agents that plan, reason, and act across multi-step workflows. I combine a strong foundation in classical machine learning with hands-on expertise in the agentic AI stack that defines the current generation of applied AI work. LLM & Agentic AI Engineering: - Frontier and open-weight models: GPT, Claude (Opus/Sonnet), Gemini, Kimi, GLM, Llama, DeepSeek, and Qwen - Agent orchestration frameworks: LangGraph, CrewAI, and AutoGen, plus the Model Context Protocol (MCP) for connecting agents to tools, APIs, and live data sources - Retrieval-Augmented Generation (RAG): vector databases (Pinecone, Chroma, Weaviate, pgvector), embedding pipelines, hybrid search, and reranking - Multi-agent system design: task decomposition, memory and state management, guardrails, and human-in-the-loop escalation so agents fail safely - Evaluation and observability: automated eval pipelines, tracing, prompt optimization, and monitoring for reliability, cost, and latency in production Machine Learning & Data Foundations: - Proficient in Python and SQL/NoSQL, with deep experience in TensorFlow, PyTorch, and Scikit-learn - Strong grounding in statistical modeling and neural network architectures (CNNs, RNNs, Transformers), applied alongside modern generative and agentic approaches - Skilled at choosing the right tool for the problem — classical ML, deep learning, or LLM-based — rather than defaulting to the newest one Deployment & Infrastructure: - Production deployment with Docker and Kubernetes across AWS, Google Cloud, and Azure - CI/CD for ML/LLM pipelines, scalable and secure model serving, and cost/performance optimization for systems running at scale Beyond the tech stack, I bring a problem-solving mindset and the ability to translate complex AI concepts into clear, actionable guidance for technical and non-technical stakeholders alike. The field moves fast, and I stay current with new models, frameworks, and best practices so the systems I build keep performing as the technology evolves. Whether you need an autonomous agent built from the ground up, an existing LLM application hardened for production, a RAG pipeline that retrieves the right context every time, or broader AI strategy and implementation support, I'd be glad to help you get there.

I’m a Mobile App Developer with hands-on experience building and publishing production-ready apps for both iOS and Android — from initial development to final App Store and Play Store submission. I specialize in turning ideas or existing web apps into fully functional mobile applications, handling everything end-to-end including authentication, payments, push notifications, and backend integrations. 🚀 What I Do 📱 Build and publish mobile apps (iOS & Android) 🔄 Convert web apps into native apps using Capacitor 🔐 Implement secure authentication (Firebase, Google, Facebook, LINE, Apple, Phone OTP) 💳 Integrate payments (Stripe, 3D Secure, custom flows) 🔔 Set up push notifications using Firebase ☁️ Full Firebase integration (Auth, Firestore, Functions, Storage) 🎬 FFmpeg-based video & audio processing tools 💡 Mobile Development Expertise Capacitor & Cordova (custom plugins, native integrations) Native iOS (Objective-C) & Android (Java) Ionic, Angular, Vue (Quasar) REST API & backend integrations I ensure apps are: ✔ Stable and production-ready ✔ Fully compliant with App Store & Play Store guidelines ✔ Optimized for performance and scalability 🎯 Real Experience Built and published cross-platform apps from scratch Implemented multiple authentication systems across apps Integrated secure payment flows including Stripe Developed FFmpeg-based tools for video/audio processing Delivered apps with real users and production usage 🤝 Why Work With Me Clear communication and fast response Focus on long-term, scalable solutions Experience handling complete app lifecycle (dev → publish → maintain) Reliable delivery with attention to detail If you’re looking for someone who can take your app from idea to live on the App Store/Play Store, I can help you build it the right way.

I’m an AI Agent & Integration Engineer with 3+ years of experience building production AI systems that connect LLMs to real business data, tools, APIs, and workflows so AI can reliably operate inside real products and businesses. I specialize in AI Agents, RAG, MCP servers, LLM applications, AI automation, and Python backends, helping startups and technical teams turn AI prototypes into reliable systems that can safely work with databases, internal knowledge, APIs, documents, and business workflows. What I build ★ AI Agents & AI Automation Multi-agent systems, tool-calling agents, LangGraph workflows, state machines, memory, routing, retries, guardrails, and human-in-the-loop workflows. ★ RAG & Enterprise Knowledge Systems Production RAG pipelines, document ingestion, hybrid/vector search, Graph-RAG, retrieval optimization, citations, access control, and hallucination guardrails. ★ MCP Servers & AI Tool Integration Secure Model Context Protocol (MCP) servers that connect AI agents to PostgreSQL, APIs, internal systems, and business tools with controlled permissions and safe execution. ★ LLM & Generative AI Applications OpenAI, Anthropic, Gemini, and AWS Bedrock integrations; structured outputs, prompt engineering, caching, fallback systems, evaluation, and cost optimization. ★ Text-to-SQL & AI Data Assistants Natural-language analytics over PostgreSQL and other databases with SQL validation, schema-aware RAG, read-only execution, query auditing, and security controls. ★ Document AI, OCR & Computer Vision Large-scale OCR preprocessing, document extraction, YOLO-based detection/tracking, image processing, and AI-powered video analysis. Recent production work ★ Built a secure Text-to-SQL MCP server for PostgreSQL with AST-based SQL validation, read-only database transactions, statement timeouts, query cost controls, schema-aware RAG, REST API support, 158 tests, and 100% test coverage. ★ Built a Text-to-SQL analytics assistant over a 3M-row PostgreSQL sales database using AWS Bedrock, with SQL validation, intent matching, automatic retry, query caching, fiscal-calendar date resolution, session memory, and an audit interface. ★ Built Rabt, a Graph-RAG engine for codebases, using AST analysis and runtime instrumentation to identify the minimal context required by an LLM, reducing context by 99.2% compared with full-repository baselines. ★ Built a 20M+ document OCR preprocessing pipeline using Python/OpenCV with parallel processing and resume-safe batch execution for large-scale legal/property records. ★ Built a YOLOv8 + EfficientNet computer-vision pipeline for sports video analysis, achieving 0.954 mAP50 and 96.3% validation accuracy and reducing manual footage-review time by 70–90%. ★ Built production RAG applications with FastAPI, LangChain, Pinecone, OpenAI APIs, RBAC, hybrid retrieval, and citation-based hallucination guardrails. My engineering approach I care about what happens after the demo. That means: * Secure tool access and permissions * Deterministic validation around LLM outputs * Reliable fallback and retry mechanisms * Efficient retrieval and context management * API and database security * Observability and testing * Token and infrastructure cost control * Clean, modular Python * Dockerized and production-ready deployments I don't just connect an LLM API and call it an AI system. I design the surrounding infrastructure that makes the system reliable enough to use with real users and real business data. Core stack Python, FastAPI, AI Agents, LangGraph, LangChain, RAG, Graph-RAG, MCP, LLMs, Generative AI, OpenAI, Anthropic, Gemini, AWS Bedrock, PostgreSQL, SQL, Vector Databases, Docker, REST APIs, PyTorch, YOLO, OpenCV, OCR, Computer Vision, Next.js, React, TypeScript. If you're building an AI product, internal AI assistant, agentic workflow, RAG system, MCP integration, Text-to-SQL application, or AI automation pipeline, I can help take it from prototype to production.

🚀 Transforming Real-World Problems into Intelligent Solutions with AI I'm Hana, a Computer Engineer, researcher with accepted papers and award-winning AI developer with hands-on experience in autonomous systems, computer vision, and data science. I’ve led and contributed to multiple real-world AI projects, from autonomous racing vehicles to AI-powered CCTV monitoring, blending software and hardware expertise with research-backed development. What makes me stand out? ✅ Multidisciplinary Expertise: From ROS & robotics to deep learning (YOLO, CNNs), I bring a full-stack AI approach. ✅ Fast & Curious: I learn quickly, adapt even faster, and deliver results with precision. ✅ Strong Coding Skills: Python, C++, TensorFlow, PyTorch, OpenCV, Scikit-learn. ✅ Deployment-Oriented: I don’t stop at models — I integrate them with dashboards, IoT streams, or edge devices. My core services include: 🔹 Object Detection & Tracking (YOLO , SSD, OpenCV) 🔹 AI for CCTV & Smart Surveillance Systems 🔹 Autonomous Navigation & Simulation (ROS2, AutoDrive) 🔹 EEG Signal Processing & Brain-Computer Interfaces 🔹 Data Analysis & Predictive Modeling 🔹 Dashboard Integration (Plotly, Streamlit) If you're looking for someone who combines technical depth with a get-things-done mindset, let's build something smart together. 📬 Feel free to send a message — I’m excited to hear about your project!