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🥇 AI Engineer & Data Scientist | 1+ Year Experience I’m an AI Engineer and Data Scientist passionate about building intelligent, automated, and data-driven systems that solve real-world problems. With expertise in Machine Learning, Generative AI, NLP, Computer Vision, and workflow automation, I help startups and enterprises create AI solutions that scale and deliver measurable impact. What I Can Deliver ➤ AI Agents & Chatbots • RAG-based multi-PDF and document Q&A systems using LangChain, FAISS, HuggingFace, and Gemini AI • Custom AI agents and chatbots powered by OpenAI, GPT, Claude, and Mistral • Voice and calling agents with Retell AI and real-time API integrations • CRM & email automation integrated with Slack, Gmail, and HubSpot ➤ Computer Vision & Image Analysis • Deep learning-based image classification, object detection, and feature extraction • Convolutional Neural Networks (CNNs), transfer learning, and data augmentation for high-accuracy predictions • Real-time image processing deployed with Flask/Django apps • Projects include pet image classification, facial attribute detection, and AI-based image analytics ➤ Workflow Automation & Orchestration • Intelligent automation pipelines using Python, n8n, Make.com, and Zapier • Lead management, reporting, and data syncing workflows • API, database, and CRM integrations to automate repetitive tasks ➤ Machine Learning & Data Science • NLP, deep learning, predictive modeling, and feature engineering • LLM fine-tuning (BERT, GPT, HuggingFace Transformers) • Recommendation systems, forecasting, and regression models • Model deployment with Flask, FastAPI, Django, and Docker ➤ Data Engineering & Business Intelligence • ETL processes, SQL pipelines, and cloud integration (Azure) • Real-time dashboards and analytics using Power BI & Tableau • Data-driven reporting and KPI tracking Technical Skills Python, Flask, Django, JavaScript, HTML, CSS, OOP NumPy, Pandas, Scikit-learn, TensorFlow, Keras, PyTorch, HuggingFace, FAISS, OpenAI API, NLP, RAG, Computer Vision n8n, Make.com, Zapier, Retell AI, API Integrations, Workflow Automation SQL, ETL, REST APIs, Web Scraping, Azure, Docker Git, GitHub, Jupyter Notebook, Google Colab, VS Code I specialize in building AI systems that automate processes, analyze visual and textual data, deliver actionable insights, and enhance business efficiency. 📍 Available for short-term & long-term projects | NDA-Compliant | Reliable & Collaborative

I am a Senior AI/ML Engineer with 8+ years of experience designing and deploying scalable Artificial Intelligence systems. I specialize in Large Language Models (LLMs), Generative AI, Deep Learning, NLP, and AI-powered applications that help businesses automate processes, extract insights from data, and build intelligent products. I have strong experience building AI-powered platforms, chatbots, recommendation systems, computer vision solutions, and LLM-based applications using modern frameworks and cloud infrastructure. My expertise includes end-to-end AI system development from research and model training to production deployment and monitoring. What I can help you build • LLM Applications (GPT, LLaMA, Claude, Mistral) • RAG Systems with vector databases (Pinecone, FAISS, Weaviate, ChromaDB) • AI Chatbots & Conversational AI for web, Slack, WhatsApp, Telegram • Generative AI solutions (text, image, and code generation) • Custom NLP pipelines (NER, sentiment analysis, classification, summarization) • Deep Learning models for prediction, automation, and data intelligence • Computer Vision systems (object detection, image classification) • Agentic AI systems using LangChain, LangGraph, and LlamaIndex • Model training, fine-tuning and optimization (LoRA, PEFT, quantization) • MLOps pipelines and scalable AI deployment Technologies & Tools: AI / Machine Learning TensorFlow, PyTorch, Keras, Scikit-learn, Hugging Face LLM & GenAI OpenAI API, LangChain, LlamaIndex, Transformers, RAG architectures Deep Learning CNN, RNN, LSTM, Transformers, GANs, Autoencoders Vector Databases Pinecone, FAISS, Weaviate, Milvus, Qdrant Cloud & Infrastructure AWS, GCP, Azure, Docker, Kubernetes, CI/CD Languages Python, Bash/Shell scripting Why work with me ✔ 8+ years of AI/ML experience ✔ Production ready AI systems ✔ Scalable architectures for startups and enterprises ✔ Strong research + engineering background ✔ Clean, maintainable, well-documented code If you're looking to build AI products, LLM applications, intelligent chatbots, or scalable machine learning systems, feel free to reach out.

I build AI powered MVPs, SaaS platforms, and business automation solutions that help founders launch faster, validate ideas sooner, and scale with confidence. Too many startups and businesses spend months developing products that miss deadlines, exceed budgets, or fail to solve the right problem. Others struggle to integrate AI effectively, ending up with disconnected tools, unreliable workflows, and poor user experiences. With years of experience in software development, business systems, and technology implementation, I help founders, startups, agencies, and growing businesses turn ideas into production ready AI solutions. My approach focuses on rapid validation, clean architecture, scalable development, and measurable business outcomes so you can move from concept to launch without unnecessary complexity. Whether you need an AI MVP, intelligent automation platform, SaaS application, Enterprise SaaS platform, Enterprise Knowledge Retrieval System, custom AI powered workflow, AI chatbot, Multi-Agent System, or Agentic Workflow solution, I combine modern development frameworks with practical business insight to deliver solutions that create real value and long term growth. My experience spans AI, LLM, & Data Engineering, AI Governance, Prompt Engineering, RAG (Retrieval-Augmented Generation), Semantic Search, Embeddings, Vector Databases, Function Calling, MCP (Model Context Protocol), OpenAI GPT-5, Claude, Gemini, DeepSeek, LangChain, LangGraph, and LlamaIndex to build intelligent systems that automate business operations and enhance decision-making. Core Expertise • AI MVP Development (Launch Ready Products) • Claude Code Development & AI Agent Systems • Lovable Application Development • Base44 Application Development • Full Stack Web Application Development • AI Powered SaaS Platforms • Custom AI Integrations & APIs • Business Process Automation • AI Workflow Design & Implementation • Chatbots & Conversational AI Solutions • Multi-Agent Systems • Agentic Workflows • Enterprise Knowledge Retrieval Systems • RAG (Retrieval-Augmented Generation) • Semantic Search & Embeddings • Vector Database Architecture • Prompt Engineering • Function Calling & MCP Implementation • CRM & Business System Integration • Database Architecture & Optimization • Cloud Deployment & Scalable Infrastructure • Product Strategy & Technical Consulting • Rapid Prototyping & MVP Validation • Front End Development (React, Next.js, JavaScript, TypeScript) • Back End Development (Node.js, Python, APIs) Technology Stack & Platforms AI, LLM, & Data Engineering: OpenAI GPT-5, Claude, Gemini, DeepSeek, LangChain, LangGraph, LlamaIndex, Prompt Engineering, RAG (Retrieval-Augmented Generation), Semantic Search, Embeddings, Function Calling, MCP (Model Context Protocol), Multi-Agent Systems, Agentic Workflows, AI Governance, AI Chatbot / Artificial Intelligence, Enterprise Knowledge Retrieval Systems, Vector Databases. Languages & Frameworks: Python, JavaScript, HTML, CSS, Tailwind CSS, Node.js, Django, FastAPI, Next.js 14 (App Router). Backend, Databases, & Architecture: PostgreSQL, MySQL, SQLite, Redis, RabbitMQ, Celery, GraphQL, RESTful API, WebSockets, SQL, JWT Authentication, OAuth2, Supabase (including Auth & Storage), RLS (Row-Level Security) Policies, Distributed Systems, Event-Driven Architecture. Cloud, DevOps, & Infrastructure: AWS (Amazon Web Services), Docker, Kubernetes, Terraform, Cloudflare, Vercel, GitHub Actions, CI/CD Pipelines, Databricks, MLflow, Prometheus, Grafana, Infrastructure as Code (IaC), Environment Variables. Core Features & Domain Context: Workflow Automation, Document Ingestion Pipelines, File Uploads / Image Handling, Form Wiring, Pagination / Filtering, Affiliate Links, Candidate Registration / Review Systems, HR Web Application / Data Management. What Clients Value ✔ Clear communication and dependable delivery ✔ Strong business and technical perspective ✔ Scalable, maintainable code architecture ✔ Fast execution without sacrificing quality ✔ Practical AI solutions focused on ROI and business results If you're looking for an experienced AI developer who can transform your idea into a working product, send me a message and let's discuss your project goals or we can hop on a quick call and I can tell you the best problem to solve first and the fastest path to getting started. Replit, Replit AI, Replit Developer, Replit Development, Replit AI Development, Replit AI Coding, Replit Deployment, Replit Debugging, Replit AI Debugging, Replit Full Stack, Replit SaaS Development, Replit MVP Builder, Replit AI Builder, Replit Automation, Replit Backend Development, Replit Frontend Development, AI SaaS, AI SaaS Development, AI SaaS Builder, AI SaaS MVP, AI SaaS MVP Developer, AI App Development, AI App Builder, AI Product Builder, AI Startup Builder, AI MVP Builder, Startup MVP Development, MVP Development, Rapid MVP Development, Rapid App Development, Production Ready MVP, Production Ready SaaS, SaaS

Most AI developers can build a prototype. Very few can optimize and deploy multimodal LLMs on Jetson Orin Nano at real-time speeds, architect enterprise-grade RAG systems across 3,000+ SQL tables, fine-tune state-of-the-art vision-language models, or serve high-throughput inference with vLLM and SGLang at production scale. That’s what I do. I’m a Senior AI/ML Engineer specializing in: • Production LLM Systems & Agentic AI • Edge AI & High-Performance Model Optimization • Computer Vision & Real-Time Multimodal AI • Distributed Inference & AI Infrastructure I don’t just wrap APIs I engineer AI systems from the model architecture and optimization layer all the way to scalable production deployment. What makes my work different: → I build multi-agent AI systems that feel invisible to users. Designed a dual-LLM tutoring architecture where a speech-to-speech AI tutor interacts live with students while a hidden orchestration LLM performs real-time prompt injection, memory routing, retrieval planning, and response control completely transparently. → I optimize and deploy AI where most teams fail. Fine-tuned PaliGemma, converted it to ONNX, applied INT8/FP16 quantization and TensorRT optimization, reducing model size by 65% with <2% accuracy loss, then deployed it on Jetson Orin Nano, Raspberry Pi, edge devices, and mobile hardware for real-time inference with extremely limited compute. → I build enterprise RAG systems that reason not just retrieve. Architected a multi-stage multilingual RAG pipeline using HyDE, recursive retrieval, schema-aware chunking, metadata filtering, hybrid search, reranking, graph-based retrieval, chain-of-table reasoning, and long-context orchestration across 3,000+ enterprise SQL tables and 150+ warehouses. → I engineer high-performance LLM inference infrastructure. Built scalable inference pipelines using vLLM, SGLang, ONNX Runtime, TensorRT-LLM, DeepSpeed, FlashAttention, speculative decoding, paged attention, KV-cache optimization, and distributed GPU serving for low-latency, high-concurrency AI systems. → I deliver video AI and lip sync systems beyond standard open-source baselines. Fine-tuned LatentSync on a 4,000-sample dataset, outperforming Wav2Lip, GAN-Wav2Lip, and Wav2LipHD baselines in multilingual real-time video translation with highly coherent visual speech synchronization. → I design AI systems for real-world production environments. Built AI backends with FastAPI, asyncio, WebSockets, Redis queues, Kafka streaming, GPU worker orchestration, autoscaling inference services, and Kubernetes-based deployments capable of handling real-time concurrent workloads. What I build for clients: • Production-grade RAG systems with hybrid, recursive, graph, and agentic retrieval • High-throughput LLM serving using vLLM, SGLang, TensorRT-LLM, Triton Inference Server • Edge AI deployment with ONNX, TensorRT, OpenVINO, CoreML, TensorFlow Lite, ONNX Runtime • Fine-tuning pipelines (LoRA, QLoRA, PEFT, RLHF, DPO) with evaluation and benchmarking • AI agents with LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, MCP architectures • Multimodal AI systems combining vision, audio, speech, and language models • Text-to-SQL systems for complex enterprise schemas with multilingual querying • Computer Vision pipelines YOLOv8, YOLO11, SAM2, GroundingDINO, DETR, Mask R-CNN • Real-time speech AI Whisper, XTTS, RVC, voice cloning, speech-to-speech pipelines • Video AI lip sync, face reenactment, multilingual dubbing, avatar systems • Distributed AI infrastructure with Docker, Kubernetes, Ray, Celery, Redis, Kafka • AI observability, evaluation, tracing, and monitoring with LangSmith, MLflow, Weights & Biases • Vector databases and retrieval systems FAISS, Pinecone, ChromaDB, Milvus, Qdrant, Weaviate • AI surveillance and geospatial intelligence systems with real-time threat detection Core Stack: Python · PyTorch · TensorFlow ·  HuggingFace · Transformers · LangChain · LangGraph · LlamaIndex · OpenAI · Anthropic · Gemini · FastAPI · vLLM · SGLang · TensorRT-LLM · Triton · ONNX Runtime · TensorRT · OpenVINO · TensorFlow Lite · CUDA · OpenCV · MediaPipe · Whisper · YOLO · SAM · GroundingDINO · FAISS · Pinecone ·  Qdrant · MongoDB · PostgreSQL · Redis · Kafka ·  Docker · Kubernetes · GCP · Vertex AI · AWS · Azure · WebSockets If you need an AI engineer who can go deeper than most teams whether that means deploying optimized multimodal AI on constrained edge hardware, architecting enterprise RAG systems that minimize hallucinations, building scalable LLM inference infrastructure, or shipping production-grade multi-agent AI send me a message. I’ll tell you honestly what’s possible, what’s overhyped, and exactly how I’d build it.

🎁 𝗔 𝟭𝟬𝟬% 𝗿𝗲𝗳𝘂𝗻𝗱 𝗶𝗳 𝗜 𝘀𝗲𝘁 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝗲𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗰𝗮𝗻’𝘁 𝗱𝗲𝗹𝗶𝘃𝗲𝗿. 𝗡𝗼 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗮𝘀𝗸𝗲𝗱! I architect the AI systems other developers can't ship production RAG pipelines, multi-agent LLM apps and chatbots on a full-stack React, Next.js and Node.js base. 𝗣𝗿𝗲𝘃𝗶𝗼𝘂𝘀𝗹𝘆 𝗧𝗼𝗽 𝗥𝗮𝘁𝗲𝗱 𝗣𝗹𝘂𝘀 (𝘁𝗼𝗽 𝟭% 𝗼𝗻 𝗨𝗽𝘄𝗼𝗿𝗸) 🏅 AI architecture, not AI demos production multi-tenant platforms that scale 🚀 About I architect production AI agent systems multi-tenant LLM orchestration, agentic RAG, voice AI, and MCP servers for SaaS founders and agencies who need real systems, not chatbot demos. 15+ years of full-stack engineering means your AI doesn't just work in a Loom video; it scales, bills, and survives real traffic. 🌟 What I Deliver ✅ AI Agent Platforms Multi-tenant orchestration with multi-LLM execution (OpenAI, Anthropic, Gemini), agentic RAG, MCP servers, embeddable widgets, LangSmith tracing, cost/KPI analytics ✅ Voice AI & Conversational Agents Production voice with VAPI, ElevenLabs, Twilio inbound/outbound calling, transcription, workflow triggers, per-call cost attribution ✅ AI Workflow Automation n8n-powered agentic workflows connecting LLMs, CRMs, payments, and 70+ APIs into autonomous business processes ✅ Multi-Tenant SaaS Infrastructure Laravel/Node backends with per-tenant isolation, subscription billing, role-based access, React frontends scaling 100 to 100K users ✅ Enterprise AI Integrations Stripe, Salesforce, HubSpot, Go High Levek (GHL) ,Shopify, OpenAI, Anthropic, Gemini, ElevenLabs, VAPI, Twilio webhook routing, async queues, full observability 🏗️ What I Build AI Agent Orchestration Platforms Production multi-tenant systems that deploy, manage, monitor AI agents at scale: multi-LLM, agentic RAG (Pinecone/MongoDB), streaming chat, cost tracking, KPI dashboards, embeddable widgets. Voice AI & Conversational Automation Voice agents with ElevenLabs, VAPI, Twilio inbound/outbound calls, real-time transcription, post-call analysis, workflow triggers, per-call cost attribution. n8n Workflow Automation Builders No-code automation backed by n8n with drag-and-drop editors, multi-step agentic workflows, webhooks, 20+ tool integrations (Slack, Sheets, Asana, ClickUp, HubSpot). MCP Servers & Agent Tool Integration Custom Model Context Protocol servers giving AI agents tool-use, structured outputs, and secure access to internal data. Multi-Tenant SaaS Backends Laravel backends with per-tenant isolation, subscription billing, role-based permissions, REST APIs built to scale from first customer to thousands. React Frontends at Scale TypeScript React 18 with BLoC, real-time streaming (Socket-io + Pusher), embeddable widgets, browser extensions, visual workflow builders CI/CD deployed. API Integration Systems 70+ integrations across Stripe, Salesforce, HubSpot, Shopify, FedEx, VAPI, Twilio, OpenAI, Anthropic, Gemini webhook routing, async queues (Laravel Horizon), error tracking. 🍇 Signature Work 🔷 Studio98-ai AI Workforce Platform (flagship): Multi-tenant AI agent orchestration serving 50+ clients across sales, marketing, ops, support. LangChain orchestration with OpenAI/Anthropic/Gemini, agentic RAG via MongoDB/PostgreSQL, MCP server, n8n workflows, LangSmith tracing. React 18 frontend with 27 modules, embeddable chat widget, browser extension, visual workflow builder, real-time SSE streaming. 🔷 Rave 2.0 Multi-tenant SaaS retail platform with subscription management, analytics, drag-and-drop page builder. 🔷 Enterprise Jira Governance Multi-team Agile system for 50+ developers with workflow automation and executive dashboards. 🛠️ Core Tech Stack AI & Agents: LangChain · LangGraph · LangSmith · MCP · Agentic RAG (Pinecone, MongoDB) · OpenAI · Anthropic Claude · Gemini · Prompt Engineering Voice AI: VAPI · ElevenLabs · Twilio · Real-time Transcription Automation: n8n · Make · Webhooks Backend: Laravel 11 · PHP 8.1+ · Node.js · TypeScript · Nova · Sanctum · Octane · Horizon · Redis · MongoDB · PostgreSQL Frontend: React 18 · TypeScript · Vue.js · Inertia.js · Socket-io · Pusher Infra: Docker · AWS · GCP · Firebase · CI/CD Delivery: Scrum (PSM I) · Jira · Confluence · ACP-620 💡 Why Clients Choose Me ✅ AI architecture, not AI demos production multi-tenant platforms that scale ✅ End-to-end ownership agent architecture to deployment to team handoff ✅ 15+ years, 50+ AI agents in production, 24,000+ Upwork hours ✅ Multi-LLM, multi-tenant, multi-channel platform scale, not script scale ✅ Top Rated Plus with 100% JSS sustained, not seasonal Last updated: 21.06.2026

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.