Hire the Best deeplearn.js Professionals

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 am a Full-Stack Developer and an AI Engineer with over 3+ years of experience building production-ready web applications and recently with AI integrations such as building RAG layers for systems , making their AI have better context about the company. I have worked at an outsourcing company for over 2 years participating in large-scale projects for upwork Clients, focusing on clean architecture, performance, and reliability. My goal is simple , to go above and beyond in order to always be able to deliver more than what is expected of me, ahead of the time that is given Core Strengths: • Full-Stack Development: Skilled in both frontend and backend, delivering complete end-to-end solutions. • Modern Frontend: Experienced with React, Next.js, Angular, and Tailwind CSS for building responsive, high-quality interfaces. • Robust Backend: Fast API, Node.js, NestJS, Express, and Go (Golang). • DevOps : Dockerizing web apps of microservices and building the CI/CD pipelilne along with deployment (server configuration and setup) • Remote Collaboration: i am well experienced in working with remote clients as i have been working for an outsourcing company for over a year now Technical Skills: Frontend: React, Next.js, Angular, Svelte/SvelteKit, Tailwind CSS Backend: Node.js, NestJS, Express, Go (Golang), REST & GraphQL APIs , python Fast API Databases: MongoDB, PostgreSQL DevOps: AWS, Docker, CI/CD Pipelines Recent brief experiences in projects : • open source hospital management system (BAHMNI) very complicated project i ever participated in • An AI integration+Fullstack dev for project management tool called Flowlenz (Tech Teamz) -Spain • Developed an ATS + Live interview platform for a company called "Code IQ" , with video call features and everything if you want a reliable , consistent , dedicated developer hit me up and i will work day and night to always deliver you more than what is expected of me, ahead of schedule

I am a Machine Learning Engineer with four years of experience working across deep learning research, large scale AI systems, and production model deployment. Over the years, I have worked extensively in medical imaging, computer vision, NLP, signal processing, and large language models, building systems that range from experimental research pipelines to deployed real world AI applications. My day to day work primarily involves Python, PyTorch, TensorFlow, Keras, HuggingFace Transformers, sentence transformers, scikit learn, OpenCV, Pandas, and NumPy. I enjoy working deeply on both the research and engineering sides of machine learning, especially problems that require understanding model behavior rather than simply applying existing architectures blindly. A large part of my background is research driven. I have authored multiple peer reviewed publications in indexed journals and IEEE conferences, including publications in Neurocomputing, Healthcare Analytics, Engineering Applications of Artificial Intelligence, Telematics and Informatics Reports, and other Elsevier and IEEE venues. My research has focused heavily on explainable AI, healthcare AI, and advanced deep learning systems. Some of my published work includes CARDxnosis, an explainable knowledge driven framework for ECG diagnosis and clinical report generation, an explainable AI system for trustworthy arrhythmia detection, a CNN RNN Attention hybrid architecture for automatic modulation classification, ensemble deep learning approaches for lung cancer detection from CT scans, and SRGAN based white blood cell image generation and classification pipelines. Alongside published work, I am currently involved in research on brain tumor segmentation, ADHD and ASD classification from brain connectome graphs, epileptic seizure prediction from EEG signals, and interpretable tabular learning using graph neural networks combined with Kolmogorov Arnold Networks. Beyond research, I have substantial hands on experience building and deploying production grade AI systems. One of my major recent projects was LaborBERT v4, a domain adaptive transformer fine tuning system processing hundreds of thousands of records through a large scale training pipeline. The project involved multiple experimental setups including contrastive learning, masked language model pretraining, temporal contrastive learning, cross attention based fusion, multi task training, and Matryoshka Representation Learning. I have also built hybrid embeddings plus LLM systems for taxonomy mapping using OpenAI embeddings alongside locally hosted LLaMA and Mistral models through Ollama. In addition, I have worked on deployed clinical AI systems and a portable on device diagnostic AI solution with embedded deep learning models for point of care inference, which gave me valuable experience in optimization, deployment constraints, inference design, and production reliability. My broader project portfolio includes vehicle detection using Mask R CNN, human activity recognition on the Kinetics 700 dataset, facial keypoint detection with MultiRes UNet, semantic segmentation pipeline redesign, Stable Diffusion based image editing workflows, toxic comment classification, RASA based conversational AI systems, and large scale scraping and automation pipelines using Playwright and Selenium. I have also worked with Flask and Django based deployment pipelines and cloud hosted ML systems. From an engineering perspective, I care strongly about clean and maintainable systems. I follow disciplined workflows involving modular code design, Git based version control, reproducible experimentation, structured evaluation, bootstrap validated metrics, and detailed documentation. I am also comfortable preparing scientific reports, research papers, and journal submissions using both LaTeX and Word. What ties all of this together is that I genuinely enjoy solving difficult technical problems, especially the kind that require balancing research depth with practical engineering constraints. I am most motivated by projects where thoughtful experimentation, careful system design, and real world usability matter equally.

🎯 ABOUT ME I build AI-powered automation workflows and full-stack web apps for startups. Specialized in n8n automation, RAG pipelines (Qdrant, LLMs), and AWS deployments. 6 clients, 6 projects completed. Direct contractor with a track record of delivering production-grade solutions that solve real business problems—not just code. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚡ WHAT I DELIVER 🤖 AI & Automation Workflows → Design & build n8n workflows (APIs, webhooks, error handling, retries) → Create RAG pipelines (embeddings, vector DBs, LLM integrations) → Automate business processes: data ingestion, ETL, document processing, multi-step approvals 💻 Full-Stack Web Applications → React / Next.js frontends (SSR, SSG, responsive, accessible) → Node.js / Express backends (REST APIs, microservices, real-time systems) → Cloud-native architectures on AWS (EC2, Lambda, RDS, S3, SQS/SNS) ⚙️ Scalability & Performance → Clean code architecture, CI/CD pipelines (GitHub Actions), Docker containerization → Database design (PostgreSQL, MongoDB, Supabase) → Security best practices (IAM, HIPAA-conscious data handling) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✨ RECENT PROJECT HIGHLIGHTS ✓ WhatsApp AI Agent + RAG — Integrated document upload, Gemini/OpenAI embeddings, Qdrant vector storage, Cohere reranking. Delivered answers via WhatsApp with <2s latency. ✓ Multi-Tenant SaaS Dashboard — Role-based access control, modular architecture, dynamic module loading. Scaled to handle healthcare workflows with HIPAA compliance. ✓ n8n Automation on AWS — Deployed error-handling workflows with logging, retries, and idempotency. Reduced manual workload by 70%. ✓ Full-Stack Health Platform — ETL pipelines, real-time Supabase features (presence, broadcast), optimized PostgreSQL queries. 35% load time improvement after migration to Next.js SSR. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔧 TECH STACK Frontend: React, Next.js (SSR/SSG), TailwindCSS, TypeScript Backend: Node.js, Express, NestJS, REST APIs AI & Automation: n8n workflows, OpenAI, Gemini, Qdrant, Cohere, RAG pipelines Cloud & DevOps: AWS (EC2, Lambda, RDS, S3, SQS, SNS), Vercel, Supabase, Docker, GitHub Actions Databases: PostgreSQL, MySQL, MongoDB, Supabase Other: Git/GitFlow, ETL pipelines, OCR, audio transcription ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 WHY WORK WITH ME → Direct communicator — No fluff. I understand your requirements and deliver what you ask for. → Production-ready code — Built to scale. Clean, tested, documented. → Problem solver — I don't just code features; I solve business problems. → Available for long-term partnerships — Most clients work with me on multiple projects. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 LET'S BUILD Ready to discuss your project? Message me with: 1. What you're building 2. Timeline & budget 3. Key technical requirements Let's turn ideas into working software. 𝐅𝐢𝐧𝐝 𝐦𝐞 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐤𝐞𝐲 𝐰𝐨𝐫𝐝𝐬 𝐛𝐞𝐥𝐨𝐰: API, RESTful API, GraphQL, API Integration, AWS Lambda, Amazon Web Services, Docker, Git, GitHub, GitFlow, CI/CD, Agile Software Development, JavaScript, TypeScript, ECMAScript, React, Redux, Next.js, Angular, Tailwind CSS, Bootstrap, HTML, HTML5, CSS, CSS3, Sass, Node.js, Express.js, NestJS, Prisma, Supabase, PostgreSQL, MySQL, MongoDB, DynamoDB, SQL, NoSQL, Microservices, Serverless Architecture, Event-Driven Architecture, AWS SQS, AWS SNS, AWS EC2, AWS S3, IBM Cloud, IBM Watson, ETL Pipelines, Automation, n8n, Webhooks, Web Development, Web Applications, Landing Pages, Responsive Design, UI/UX, Figma to Code, Performance Optimization, SEO Optimization, User Authentication, JWT, OAuth2, Testing, Jest, End-to-End Testing, API Security, Cloud Deployment, Server-Side Rendering, SSR, Static Site Generation, SSG, SaaS Development, Data Processing Pipelines

🚀 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 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 — 𝗻𝗼𝘁 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲𝘀. Most AI projects look great in a demo and break when they meet real work. The agent routes to the wrong tool. The RAG retrieves the wrong chunks. The voice latency kills the conversation. The pipeline runs in a notebook but never makes it to production. That's where I come in. In 2 months at a Dubai tech company, I built and deployed 6 production AI services from zero infrastructure. Voice assistants, document tools, RAG pipelines, agent workflows — all live, all on AWS, all monitored with LangSmith. ─────────────────────────── WHAT I'VE SHIPPED ─────────────────────────── ◆ Voice assistant with 7-agent architecture — real-time speech (Deepgram + LiveKit), Text-to-SQL, RAG document Q&A, multi-agent routing via LangGraph ◆ AI Status Reports — optimized 17s → 2.1s, beat the 5-second requirement by 58% ◆ Document Classifier — bulk upload with Gemini 2.0 Flash. $0.0002/doc, 100% accuracy in production testing ◆ COSTRA — 5-agent BOQ extraction (internal tool) — vision models (Claude 4.5 Sonnet, Gemini 3 Pro) extracting data from construction PDFs, exports to Excel/JSON ◆ Medical RAG (graduation research, distinction with honors) — F1: 79.3 / EM: 69.4 on LitQA v2 with hybrid retrieval, cross-encoder reranking, context compression ─────────────────────────── WHAT I BUILD FOR YOU ─────────────────────────── ◆ Multi-Agent Systems — LangGraph, LangChain, tool routing, structured outputs, conditional flows ◆ Voice AI Agents — phone calls (Telnyx), browser-based (LiveKit), real-time STT/TTS (Deepgram, ElevenLabs), Silero VAD ◆ RAG Pipelines — over your documents, databases, and APIs. Hybrid retrieval, reranking, evaluation. ◆ LLM Apps & APIs — Claude, GPT-4, Gemini, Groq, or self-hosted (vLLM, Ollama) ◆ Production Deployment — FastAPI → Docker → AWS (Lambda, EC2, Bedrock) with CI/CD, Caddy reverse proxy, LangSmith monitoring ─────────────────────────── TECH STACK ─────────────────────────── Languages & Frameworks — Python, FastAPI, Flask, Next.js, React Agents & Orchestration — LangGraph, LangChain, multi-agent systems, tool calling RAG & Retrieval — Hybrid retrieval (BM25 + dense), cross-encoder reranking, context compression, chunking strategies Voice AI — Deepgram, LiveKit, Telnyx, ElevenLabs, Silero VAD, Gemini Live API LLMs — Claude, GPT-4, Gemini, Groq, AWS Bedrock, vLLM, Ollama Vector Stores — FAISS, Pinecone, Qdrant, Milvus, Chroma, pgvector Cloud & Infra — AWS (EC2, Lambda, Bedrock, API Gateway, S3, Amplify), Docker, Coolify, CI/CD Observability — LangSmith, Langfuse Backend — PostgreSQL, Supabase, Neo4j AuraDB Automation — n8n, REST APIs, webhooks ─────────────────────────── AWS Certified Cloud Practitioner. Tell me what you're trying to build. I'll tell you honestly if it's the right approach and how I'd build it.