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Hi, I`m Noor 👋 "I turn raw data into clear, actionable insights that empower better decisions and drive real results, every step of the way." I'm a Data Scientist and AI Engineer. Over the past 5+ years, I’ve worked on projects that combine data engineering, machine learning, and large language models to build intelligent, production-ready solutions. I specialize in designing end-to-end ML pipelines, developing LLM-powered applications (RAG, LangChain, Llama-2/3, OpenAI), and deploying scalable systems on Azure, Databricks, and Docker. My work often involves automating data workflows, improving prediction accuracy, and transforming complex data into clear insights. Some of my favorite projects include building an AI chatbot for e-commerce, a predictive system for event planning, and an IoT protocol translator using LLMs. I value clarity, efficiency, and collaboration and I always aim to deliver results that make a measurable impact. If you’re looking for someone who can turn your data or AI idea into a working solution, I’d be happy to help.

🚀 AI/ML Engineer | Deep Learning | LLMs | FastAPI | Streamlit | RAG Pipelines I’m Aryan Saini, a final-year B.Tech student with 2+ years of hands-on experience building intelligent systems using Python, PyTorch, and modern MLOps tools. I specialize in developing full-stack AI applications—from training custom deep learning models to deploying them via scalable APIs and interactive dashboards. 🔍 What I Can Do for You: • Build and deploy deep learning models (NLP, CV, audio) • Architect RAG pipelines & multi-agent LLM workflows • Integrate HuggingFace, Groq API, OpenAI, and custom fine-tuned models • Develop backend APIs using FastAPI or Express • Create modern UIs with Next.js + Tailwind (Streamlit for quick dashboards) • Deploy on AWS, Vercel, Render, or Docker 🛠️ Key Tools & Frameworks: Python, PyTorch, Transformers, LangChain, FastAPI, Streamlit, Docker, MongoDB, PostgreSQL, OpenAI, Groq, HuggingFace, Tavily, AWS EC2/S3 📌 Highlighted Projects: • ASTRALEARN – AI-powered study planner with LLM agents, RAG search, PDF chatbot • VoxAI – Voice cloning & speech synthesis with TTS/VC models (StyleTTS2, SeedVC) • Multimodal Sentiment Analyzer – Video-based emotion recognition system • Sign Language Recognizer – Real-time gesture classification using CNN + LSTM ✅ Why Hire Me? I’m proactive, fast with iterations, and can take your idea from concept to a fully working AI product. Whether you need help with a proof of concept, research prototype, or a production-grade app, I bring strong technical depth and clean execution. Let’s collaborate to turn your AI vision into reality!

I build and ship production AI systems that real users depend on, not demos. RAG pipelines, multi-agent LLM apps, fine-tuned models, and multimodal/OCR extraction, deployed to run 24/7 on Kubernetes and serverless GPU. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Top-Rated Plus | 100% Job Success | 4+ years ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Enterprise-grade AI for multinational companies and startups, including HIPAA-conscious healthcare workflows. I turn complex requirements into intelligent, production-ready applications that drive measurable results. ━━━━━━━━━━━━━━━━━━━━━ WHAT I DO BEST ━━━━━━━━━━━━━━━━━━━━━ Agentic AI & Multi-Agent Systems Custom architectures with LangGraph, CrewAI, and Model Context Protocol (MCP), including self-improving agents that learn from evaluation feedback. Built for real automation, not chatbot demos. Advanced RAG, Evaluation & Observability 10+ production RAG systems (self-RAG, adaptive retrieval), one serving hundreds of users across thousands of documents. Migrated Pinecone to Weaviate for better recall at lower cost. Every system ships with LLM-as-judge, retrieval metrics, and full tracing (LangSmith/Langfuse), so quality is measured, not guessed. LLM Fine-Tuning & Cost Optimization PEFT (LoRA/QLoRA), SFT, DPO, and instruction tuning. Fine-tuned a 7B Arabic model served on autoscaling serverless GPU, plus multimodal vision-language models. Cut client AI costs by up to 40% through open-source replacement and quantization, with no drop in performance. Multimodal & Document AI OCR and document-extraction pipelines across PDF, DOCX, PPTX, Excel, and images, with strong F1 on messy financial and clinical documents. Also built a temporal, multi-hop knowledge graph over an encrypted Postgres + Qdrant store with client-side encryption. AI Automation & Integrations Connecting LLMs to real business systems: n8n, Make (Integromat), Zapier, CRM automation (HubSpot, GoHighLevel, Airtable), Supabase backends, and Twilio/WhatsApp. AI that plugs into how your team actually works. Enterprise Backend & Scalable Infra Master-level Python (FastAPI, Flask), robust CI/CD, and multi-cloud deployment (AWS, Azure, GCP). Docker + Kubernetes with KEDA autoscaling, plus privacy-first, multi-tenant systems (E2EE, RBAC, audit logging), including HIPAA-conscious PHI handling. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ TECH STACK ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ Agents & LLMs: LangChain, LlamaIndex, LangGraph, CrewAI, MCP, Hugging Face (PEFT/TRL), Ollama, TGI, vLLM ▸ Eval & Tracing: LangSmith, Langfuse, LLM-as-judge, custom eval frameworks ▸ Vector DBs: Weaviate, Pinecone, Qdrant, FAISS, ChromaDB ▸ Models: OpenAI, Claude, Gemini, fine-tuned open-source ▸ Automation: n8n, Make (Integromat), Zapier, Supabase, Twilio ▸ Backend: Python (FastAPI, Flask), TypeScript/Node (NestJS, NextJS), PostgreSQL, MongoDB ▸ MLOps & Cloud: Docker, Kubernetes, KEDA, CI/CD, Airflow, MLflow; AWS (SageMaker, Lambda), Azure ML / Azure OpenAI, GCP, serverless GPU ▸ CV & Data: OCR optimization, vision-language models, Stable Diffusion, web scraping ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WHY CLIENTS PICK ME ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ Ships to production. I build AND deploy. You get systems that run 24/7 and scale, not a prototype someone else has to finish. ▸ Proven track record. Top-Rated Plus, 100% Job Success, enterprise and healthcare AI delivered end-to-end. ▸ Innovation-driven. I bring the latest (MCP, adaptive RAG, new model releases) into production. ▸ Cost-conscious. High-performance AI that optimizes spend without compromising quality. ▸ Quality-first. Production-grade code, proper testing, and evaluation built in. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Building an AI product, or need one taken from prototype to production and scaled reliably? Send me the brief and I'll tell you exactly how I'd approach it.

🏅 Top Rated AI/ML Expert on Upwork (Top 1%) 🏅 AI Product Engineer | LLM Applications | Generative AI | Agentic Workflows 🏅 10+ Years of Experience | Built Solutions for Startups, Enterprises & Global Clients 🏅 Experience Across Microsoft, Google, American Express & High-Growth Startups 🏅 Expert in Python, OpenAI, Claude, LangChain, LangGraph, AWS, SQL 🔹 LLM Applications & Generative AI Products I build end-to-end LLM-powered applications that solve real business problems—from idea to deployment. My work includes document intelligence systems, AI copilots, enterprise search, summarization engines, Q&A systems, and workflow automation platforms. Built solutions for: ✔ Due diligence copilot for investment workflows ✔ Legal AI research platforms ✔ Resume/CV parsing applications ✔ AI-powered document summarization systems ✔ Knowledge retrieval platforms using RAG 🔹 AI Agents & Workflow Automation I design intelligent AI agents that automate repetitive workflows and improve operational efficiency. Examples include: ✔ Multi-agent workflows ✔ AI automation using OpenAI, Claude, LangChain & LangFlow ✔ Process automation using n8n ✔ Document processing pipelines ✔ Internal business workflow automation 🔹 NLP & Custom Machine Learning Solutions Strong background in NLP and machine learning beyond LLMs, including: ✔ Named Entity Recognition ✔ Classification Models ✔ Clustering Models ✔ Recommendation Systems ✔ Text Processing Pipelines ✔ Custom ML model development 🔹 Predictive Analytics & Risk Modeling My foundation comes from years of building machine learning models for large enterprises including Google and American Express. Experience includes: ✔ Credit risk modeling ✔ Fraud analytics ✔ Customer segmentation ✔ Forecasting ✔ Marketing analytics ✔ Behavioral modeling 💼 Whether you're a startup building your first AI product or an enterprise looking to integrate Generative AI into existing workflows, I can help with: AI Strategy → Architecture → MVP Development → Deployment I combine deep analytical expertise with hands-on experience building real-world AI products that businesses actually use.

𝐀𝐈 𝐢𝐬 𝐨𝐧𝐥𝐲 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞 𝐰𝐡𝐞𝐧 𝐢𝐭 𝐫𝐮𝐧𝐬 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 — 𝐧𝐨𝐭 𝐰𝐡𝐞𝐧 𝐢𝐭 𝐬𝐢𝐭𝐬 𝐢𝐧 𝐚 𝐝𝐞𝐦𝐨. I design and build 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 ai systems that combine 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐚𝐠𝐞𝐧𝐭𝐬, 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧, and 𝐟𝐮𝐥𝐥-𝐬𝐭𝐚𝐜𝐤 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 into one cohesive, production-ready ecosystem. 𝐅𝐫𝐨𝐦 𝐝𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 → 𝐌𝐋 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 → 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐚𝐠𝐞𝐧𝐭𝐬 → 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 → 𝐮𝐬𝐞𝐫-𝐟𝐚𝐜𝐢𝐧𝐠 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬, 𝐈 𝐡𝐚𝐧𝐝𝐥𝐞 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 — 𝐞𝐧𝐬𝐮𝐫𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐢𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞, 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞, 𝐚𝐧𝐝 𝐛𝐮𝐢𝐥𝐭 𝐟𝐨𝐫 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐮𝐬𝐚𝐠𝐞. 🚀 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐝 (𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝) 🤖 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 I specialize in "Agentic" workflows where AI can use tools, remember context, and collaborate: Frameworks: LangGraph, CrewAI, and LangChain. Capabilities: Tool-calling, persistent memory, and multi-agent systems. Agentic Workflows: n8n-based logic for complex decision routing. Use Cases: Automated Lead Qualification, AI Support Resolution, and Operations Routing. 📊 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 & 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 I build custom ML pipelines that turn raw data into foresight: Forecasting: Predictive analytics for sales and market trends. Security: Anomaly and fraud detection systems. End-to-End MLOps: Data collection, feature engineering, model training, and monitoring. ⚙️ 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 I replace manual overhead with event-driven ecosystems: Platforms: Expertise in n8n, Make, and Zapier. Custom Logic: Deep Python-based automations for complex logic. Impact: Real-time reporting, automated sales pipelines, and AI-triggered operations. 🌐 𝐅𝐮𝐥𝐥-𝐒𝐭𝐚𝐜𝐤 𝐀𝐈 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 I provide a single point of accountability for your entire application: Backend: Production-grade FastAPI and Node JS APIs with RAG integration. Frontend: React dashboards and AI Copilot interfaces. Knowledge Bases: Vector databases like Pinecone and Weaviate for enterprise RAG. 🛠️ 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 AI & Agents: LangGraph, CrewAI, LangChain, OpenAI, Claude, Gemini. ML Systems: Python, PyTorch, Scikit-learn, Hugging Face. Automation: n8n, Make, Zapier, Custom Python Scripting. Full-Stack: React, FastAPI, Node JS, PostgreSQL, Vector Databases. Infrastructure: AWS, GCP, Docker (Microservices-ready). 💡 𝐖𝐡𝐲 𝐂𝐥𝐢𝐞𝐧𝐭𝐬 𝐂𝐡𝐨𝐨𝐬𝐞 𝐌𝐞 Systems Thinking: I design interconnected AI ecosystems, not isolated features. Production-First: No fragile prototypes. Every system is built to be secure and scalable. Direct ROI: I focus on automation that pays for itself by reducing overhead and error. 𝐋𝐞𝐭’𝐬 𝐭𝐮𝐫𝐧 𝐲𝐨𝐮𝐫 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐢𝐧𝐭𝐨 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭, 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. 𝐌𝐞𝐬𝐬𝐚𝐠𝐞 𝐦𝐞 𝐭𝐨 𝐝𝐢𝐬𝐜𝐮𝐬𝐬 𝐡𝐨𝐰 𝐰𝐞 𝐜𝐚𝐧 𝐬𝐜𝐚𝐥𝐞 𝐲𝐨𝐮𝐫 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐀𝐈.

I build machine learning, computer vision, and data science systems that are meant to be tested, improved, and used. Most of my work is in computer vision, deep learning, medical imaging, image classification, object detection, segmentation, model evaluation, and applied ML workflows. On Upwork, I have completed projects in machine vision, X-ray model development, padel analytics, sign language translation, facial image classification, and health data analysis, with 100% Job Success and 5-star client feedback. I can help with: - Computer vision models for detection, segmentation, classification, OCR, and visual inspection - Deep learning with PyTorch, TensorFlow, YOLO, CNNs, Transformers, and OpenCV - Medical imaging and healthcare ML prototypes - Sports and video analytics pipelines - Data cleaning, EDA, feature engineering, model training, and evaluation - Model debugging, threshold tuning, error analysis, and performance reporting - FastAPI, Streamlit, and dashboard apps for ML workflows - AI automation and LLM workflows when they support a real technical system I care about the parts that usually decide whether an ML project succeeds: clean datasets, reproducible experiments, useful evaluation, readable reports, and applications that are easy to test. If you need help with a computer vision, deep learning, or applied ML project, send me the details. I will tell you what approach makes sense, what can be built, and what risks to watch before development starts.