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5⭐ 𝗖𝗹𝗶𝗲𝗻𝘁 𝗥𝗲𝘃𝗶𝗲𝘄: "Highly recommended! Vivek is professional, responsive, and delivers excellent work. He quickly understood my requirements, communicated clearly throughout the project..." 🎓 𝗠𝗮𝘀𝘁𝗲𝗿'𝘀 𝗶𝗻 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 + 𝟳 𝘆𝗲𝗮𝗿𝘀' 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 | Python, OCR, n8n, OpenAI, Claude, Computer Vision Top Rated Plus AI automation engineer helping businesses turn messy documents and manual workflows into reliable automation systems using n8n, OCR, Python, OpenAI/Claude, RAG, and computer vision. I build AI systems that extract structured data from PDFs, invoices, receipts, emails, forms, scanned documents, and images — then automate the workflow into CRMs, spreadsheets, databases, or approval systems. 👉 Proof from past work: • Cut image-processing time by 94% for Magna International: 37s to 2.5s • Built n8n workflows and RAG agents saving a client 20+ hours/week • Delivered AI workflows clients described as precise, efficient, and fully aligned with requirements • Built YOLO + Python real-time sports analytics for player and ball tracking 👉 What I build: • AI data extraction from invoices, PDFs, forms, receipts, shipping labels, images, and scanned documents • n8n automations connecting Gmail, Google Sheets, Airtable, CRMs, APIs, databases, webhooks, and human review steps • Python/FastAPI backends for workflows that need more reliability than a no-code automation • OCR and document AI pipelines using OpenAI, Claude, Mistral OCR, Tesseract, RAG, vector databases, and validation logic • AI agents for classification, routing, summarization, field extraction, exception handling, and decision support • Computer vision pipelines with OpenCV, YOLO, PyTorch, TensorFlow, and real-time image/video processing 👉 Where I bring the most value: ✅ AI Automation Engineering • n8n, Python, OpenAI/Claude APIs, webhooks, and REST API integrations • Production-grade error handling, retry logic, monitoring, and human-in-the-loop review • Workflow automation for Gmail, Google Sheets, Airtable, CRMs, databases, and internal tools • Python/FastAPI backends when an automation needs more reliability than a no-code workflow ✅ AI Data Extraction & Document Processing • OCR + AI data extraction for invoices, PDFs, receipts, forms, shipping labels, and scanned documents • Document classification, field extraction, validation, and exception routing • RAG workflows for searching, comparing, and summarizing internal documents • n8n automations that move extracted data into Sheets, CRMs, databases, APIs, or approval queues ✅ GDPR-Aware Automation for EU Businesses • GDPR-aware AI automation for European teams handling sensitive documents or customer data • Privacy-conscious workflows with human review, audit trails, access control, and data-minimization thinking • On-premise or self-hosted options where documents should not be sent through unnecessary third-party tools • Clear handoff documentation so your team understands where data goes, what is stored, and what can be deleted ✅ Computer Vision & Python Automation • Computer vision pipelines with OpenCV, YOLO, PyTorch, TensorFlow, and image processing • Real-time object detection, tracking, video analytics, and quality-control workflows • Performance optimization for production systems, including latency and processing-speed improvements • Dockerized deployment, clean handoff, and documented production-ready code ✅ AI Agents & Workflow Intelligence • AI agents for classification, routing, summarization, decision support, and task automation • LLM-powered workflows using OpenAI, Claude, RAG, vector databases, and prompt optimization • Automation rescue: fixing broken n8n workflows, unreliable OCR pipelines, and fragile scripts • Long-term automation support, improvements, and 30-day post-deployment warranty Tech Stack: n8n, Python, FastAPI, Flask, OpenAI API, Claude API, RAG, AI agents, prompt engineering, webhooks, REST APIs, SQL, PostgreSQL, Docker, AWS, Google Sheets, Gmail, Airtable, CRMs, Slack, Notion, Mistral OCR, Tesseract, PDF extraction, OpenCV, YOLO, PyTorch, TensorFlow. I’m strongest when the workflow is messy: inconsistent PDFs, poor image quality, missing fields, vendor-specific formats, manual checks, broken n8n workflows, or automations that fail when real-world edge cases appear. You get rapid prototypes, production-ready code, Dockerized deployment, clear documentation, and a 30-day post-deployment warranty for issues after handoff. Send me the workflow, one sample document, or your current broken automation. I’ll outline the shortest reliable path to automate it.

WebRTC, FFmpeg, AI, OpenCV, Streamin – I fix real-time video apps and have since 2005. 13,485 hours, 14 projects, 100% Job Success. Zero abandoned. ⚡ 𝗙𝗿𝗲𝗲 𝗣𝗥𝗗. 𝗠𝗩𝗣 𝗶𝗻 𝟳-𝟭𝟰 𝗱𝗮𝘆𝘀. 𝗙𝗿𝗲𝗲 𝗰𝗼𝗱𝗲 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗿 𝟮-𝘄𝗲𝗲𝗸 𝘁𝗿𝗶𝗮𝗹 𝗼𝗻 𝗮𝗻 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗰𝗼𝗱𝗲𝗯𝗮𝘀𝗲. Netflix, HBO, EA, Nokia, the World Bank, and Harvard trust platforms I've built. These clients don't give second chances. Mine work. ⚠️ Invite me to your job – I reply within 15 minutes. 𝗦𝗲𝗹𝗲𝗰𝘁𝗲𝗱 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 → Speed.Space – live production platform for Netflix, HBO, EA, and Apex Legends. Multi-stream switching, real-time collaboration, pro codec control. Used at Paris Fashion Week and Live Nation events. Zero downtime. → ShortKlips – video project management for Nokia and the World Bank. 200K+ monthly video views. Cut client project turnaround by 30%. → VirtualCr8tive – hybrid event platform for Harvard University and U.S. Vets. → CloudDoctors – HIPAA-compliant telehealth on WebRTC. Built from concept through multiple release cycles. → YUUALL – WebRTC platform built across multiple versions, long-term engagement. 𝗪𝗵𝗲𝗿𝗲 𝗜'𝗺 𝗦𝘁𝗿𝗼𝗻𝗴𝗲𝘀𝘁 – Pipeline latency – I profile FFmpeg filter graphs and cut processing time without losing quality. – WebRTC under load – jitter buffer, codec mismatch, TURN/STUN. I find which one is yours fast. – AI on live video – TensorFlow inference under 80ms; object and audio recognition on live feeds. – FFmpeg deep work – custom filter graphs, muxing, hardware acceleration (NVENC, VAAPI, VideoToolbox). – Scale & reliability – systems handling 600M+ call minutes a month, fixed at the architecture level. 𝗦𝗽𝗲𝗰-𝗙𝗶𝗿𝘀𝘁 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 I work spec-first, not vibe-first. I write the full spec and architecture before any code. Then I drive AI agents: developer, tester, analyst – against strict guardrails to produce production-grade code. The hard real-time work I handle or verify myself: WebRTC pipelines, sub-second latency, on-device ML. You get AI speed with the stability of a senior engineer, 4-10× faster than classic coding. 𝗛𝗼𝘄 𝗜 𝗪𝗼𝗿𝗸 You describe what's broken or what you're building. I send back a diagnosis: the problem, the fix, the cost. Not a sales call. Then we scope the work together before either of us commits. My estimates average ~10% off actuals. I validate before building and run staged QA on every release. 𝗛𝗼𝘄 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗪𝗼𝗿𝗸 𝗪𝗶𝘁𝗵 𝗠𝗲 1. Build from scratch – real-time video, AI, or streaming, end-to-end. 2. Drop in on your team – I take the hard video, AI, or WebRTC modules. 3. Rescue and modernize – fix slow or unstable platforms and ship to production. I work inside the Fora Soft team – 51-200 engineers. If your project grows, a PM, QA, and more developers are already there. No solo freelancer who vanishes when scope expands. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗜 𝗨𝘀𝗲 𝗗𝗮𝗶𝗹𝘆 ◆ Real-time & video: WebRTC, LiveKit, FFmpeg, GStreamer, OpenCV, HLS/DASH, RTMP/RTSP, OBS, WebGL ◆ AI & ML: TensorFlow, PyTorch, OpenAI API, Whisper, YOLO, object and audio recognition ◆ Back-end: Node.js, Python, TypeScript, distributed systems, high-load APIs ◆ Cloud & infra: AWS, GCP, Docker, Kubernetes, CI/CD 𝗪𝗵𝘆 𝗙𝗼𝗿𝗮 𝗦𝗼𝗳𝘁 The team behind me: 914 jobs · $10M+ earned · 401K+ hours · 250+ projects since 2005 · 400+ clients across 17+ countries · Top WebRTC & Telecom Developer 2022 and 2024 · 1 of 400 devs passes our internal selection. 𝗖𝗹𝗶𝗲𝗻𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 "We enjoyed working with Andrey. He saw himself as part of our team and proactively contributed. Even last-minute requests were handled in a timely, constructive way. Many of his proposals were better than our own ideas." – client, YUUALL (WebRTC platform) ⚠️ Describe what's broken. I'll tell you what's wrong, how to fix it, and what it costs.

I build AI systems that survive production — not demos that impress once and break under real users. As CTO at two AI companies (Quickgen Technologies and QuickComm AE), I architect and ship LLM, computer vision, and real-time systems for startups and enterprise clients. 6+ years of experience. MS in Computer Science (NUST). WHAT I BUILD AI Agents & LLM Systems Multi-agent workflows (LangChain, LangGraph, CrewAI), RAG pipelines with vector search, and production integrations with Gemini, GPT, Whisper, and Deepgram — including real-time streaming pipelines under 300ms latency. Computer Vision & Edge AI Object detection and tracking (YOLOv8), OCR pipelines, and edge deployment on constrained hardware for IoT and wearable products. Full-Stack & Cloud Infrastructure FastAPI, Flask, and Django backends, PostgreSQL, Redis, WebSocket-based real-time systems, and CI/CD deployment on Azure and AWS. Mobile & IoT Offline-first apps in React Native and Flutter, with BLE/hardware integration for connected products. PROVEN IN PRODUCTION Recent work includes a real-time AI voice platform processing live speech with sub-300ms latency (QuickComm), an IoT rehabilitation platform combining wearable sensors with ML-driven motion

Have an AI project that needs to actually work in production, not just look good in a demo? I'd love to hear about it. I architect the kind of AI systems that teams rely on every day: autonomous agents that run your operations, RAG pipelines that give you real answers from your own data, voice interfaces that handle client intake in real time, and AI video and content engines that produce at scale. My clients span tech, legal, finance, medical, and media, and what connects them is a need for AI that performs reliably under real constraints. What I Build: Autonomous Agents & Multi-Agent Orchestration Stateful AI agents with conditional routing, graph-based workflows (LangGraph), and tool-calling architectures designed for production workloads. I build with safety and governance from day one: human approval loops, strict compliance guardrails (HIPAA/COPPA), and containerized execution to keep your agents predictable and under control. Whether you're inspired by what you've seen from OpenClaw or need something more controlled and enterprise-ready, I can help you architect the right solution. Example: a Clinical Dispatch Agent handling patient intake, lead qualification, and live CRM updates end-to-end. High-Fidelity RAG & Context Engineering Beyond basic vector search. I apply context engineering and advanced prompt engineering techniques including metadata filtering, vertical slicing, and structured retrieval to maximize signal and minimize hallucination. I work with vector databases like Pinecone, Qdrant, and pgvector alongside Supabase and PostgreSQL for hybrid storage. The goal is audit-grade precision, not "good enough" answers. Example: a GovTech Opportunity Analyzer on AWS that synthesizes federal contract requirements with high accuracy. Real-Time Voice & Multi-Modal AI Low-latency voice and vision interfaces built on Retell AI, LiveKit, Twilio, ElevenLabs, Deepgram, and Gemini. I handle the full stack from speech-to-text through intent processing to response generation, deployed on infrastructure like Next.js, Vercel, and Supabase for fast, reliable performance. Example: a 3D voice-powered learning companion for children featuring real-time adaptive mentoring, long-term memory, and COPPA-compliant safety, architected from zero to production. AI-Powered Video & Content Pipelines End-to-end content automation that orchestrates across the current generation of models (Kling, Sora, Veo, LTX-2, HeyGen, WaveSpeed), routing each task to the right model based on quality, cost, and use case. My pipelines handle everything from web scraping and script generation to video sourcing, voice synthesis (ElevenLabs, Amazon Transcribe, Nvidia Parakeet), and brand-consistent output at scale. Example: a video engine serving 50+ publishers, automating metadata and production for 1,000+ assets monthly. How I Work: I use Claude Code, Codex, OpenCode, and Cursor alongside agentic development workflows to ship robust systems faster than traditional development, with the engineering judgment to know when to trust AI output and when to rewrite it. I bring the same ownership mindset to client projects that I bring to building my own products from zero to production. My core stack is Python, TypeScript, FastAPI, Next.js, React, and Node.js, with infrastructure across GCP, AWS, Vercel, and on-premise deployments for clients who need data to stay on their own hardware. Sound like a fit for what you're working on? Drop me a message with a quick overview of your project and I'll get back to you promptly.

Most developers will build exactly what you spec. The problem is that what gets specced is rarely what actually solves the business problem. The gap between those two things is where AI projects fail, and closing that gap before writing any code is how I work. I have been building production AI systems and full-stack platforms for several years. Not prototypes. Systems that handle real users, real data and real consequences when something breaks. TaxForce is a 37-agent system running inside a real CPA firm covering document intake, risk scoring, audit defense, deadline tracking and client communication. The Facebook Self-Healing Ads platform is a closed-loop AI system that monitors, diagnoses and fixes campaign performance without human intervention. PAM AI handles thousands of concurrent dealership calls across 8 plus CRM integrations. Dentva is a HIPAA-compliant SaaS handling patient scheduling in production daily. These are not templates strung together, they are properly engineered systems with error handling, logging and recovery paths built in from the start. Before this I spent a year and a half at Turing doing RLHF and model evaluation on the OpenAI and Claude model families. That experience shapes how I evaluate AI output and design systems that behave reliably rather than impressively in demos. On the full-stack side I was CTO on RIZZ, a React Native dating app that grew past 100K users. I have worked on Junia AI, Slides and TopSocialAI across multi-tenant architecture, real-time systems and mobile at scale. These are confirmed production experience not claimed skills. The stack I work in daily is Next.js, React, TypeScript, Python FastAPI, Node.js, Supabase, PostgreSQL, LangChain, LangGraph, OpenAI, Anthropic API, Retell AI, Vapi, Twilio, ElevenLabs, Deepgram, n8n, Make and AWS. If you are building something in agentic systems, AI automation, full-stack SaaS or voice AI and you want someone who will tell you honestly what the right approach is before agreeing to build it, that is the conversation I am interested in having.

🚀 🥇 Expert-Vetted | Hands-On AI/ML Engineer | I Build LLM Apps, RAG Systems & AI Agents (MCP, LangGraph) | Python, AWS, GCP, Azure | Healthcare & FinTech 👁‍🗨 Overview I build and ship production AI systems myself, end to end. No handoffs, no delegation: I design the architecture, write the code, and stay on it until it is deployed, monitored, and generating ROI. I bring 15+ years of hands-on AI/ML engineering, a PhD in Machine Learning from Iowa State University, and a Master's in Computational Neuroscience from UC San Diego. ✅ What I Build: • LLM Applications: RAG pipelines, chatbots and copilots, document AI, semantic search, structured data extraction • AI Agents: Multi-agent systems, MCP (Model Context Protocol) tool integrations, LangGraph orchestration, function/tool calling, agentic workflow automation • Model Customization: Fine-tuning (LoRA/QLoRA, RLHF/DPO), prompt optimization, evals and guardrails, open-weight model serving (vLLM) • Healthcare AI: Clinical trial automation, medical document generation, HIPAA-compliant systems • Full-Stack AI Products: Python/FastAPI backends, React frontends, Kubernetes, CI/CD across AWS, GCP, Azure 🎯 Recent Hands-On Builds: • Engineered a clinical trial intelligence system for enterprise pharma: ingested, embedded, and indexed 100K+ trials with multi-index, multi-LLM RAG and advanced PDF parsing, powering Q&A, chat, and benchmarking • Built a GenAI product that drafts 100+ page regulatory clinical trial protocols (95% of the full M11 document), with multi-agent validation, consistency, and styling checks • Coded and deployed an ICD-10 billing code prediction model on GCP and an EHR-integrated physician sidebar on AWS EKS • Rescued a failing third-party ML platform, refactored it, and took it to production on AWS at ResMed (NYSE: RMD), enabling their first commercial AI healthcare product • Built ML-powered ad targeting and recommendation systems generating $100K+/month, plus AI products earning $1M+ revenue in year one 💼 Industry Expertise: • Healthcare/Pharma: Clinical trials, EHR API integration, medical AI, FDA-regulated software • FinTech: Real-time fraud detection, card-linked platforms, transactional APIs (MasterCard and Visa partnerships) • Enterprise SaaS and Retail/E-commerce: Multi-tenant APIs, recommendation engines, customer analytics 🔧 Technical Stack: AI/ML: GPT-5, Claude, Gemini, Llama, DeepSeek, Qwen; fine-tuning (LoRA/QLoRA, PEFT, RLHF/DPO); RAG and GraphRAG, hybrid search, rerankers, embeddings Agents: MCP, LangGraph, LangChain, LlamaIndex, CrewAI, OpenAI Agents SDK, structured outputs, tool calling Languages: Python, TypeScript/JavaScript, SQL, Java, Go Frameworks: PyTorch, Hugging Face, FastAPI, React, TensorFlow, Scikit-learn Serving & MLOps: vLLM, Ollama, AWS (SageMaker, Lambda, ECS/EKS), GCP (Vertex AI), Azure AI Foundry, Kubernetes, Docker, MLflow, Weights & Biases Evals & Observability: LangSmith, Langfuse, RAGAS, guardrails, LLM cost optimization Databases: PostgreSQL/pgvector, Pinecone, Qdrant, Weaviate, ChromaDB, Elasticsearch, MongoDB, Redis 📊 Quantifiable Impact: • 100K+ clinical trials processed, indexed, and made queryable for enterprise users • 100+ page medical documents generated with regulatory compliance • 94% accuracy in crisis detection and 73% engagement increase for a nonprofit youth chatbot • 10X subscriber growth driven by models I built and deployed • $100K+/month revenue from ML-powered ad targeting 🎓 Credentials: • PhD, Machine Learning (Iowa State University); M.Sci., Computational Neuroscience (UC San Diego) • IBM Certified: RAG and Agentic AI; Deep Learning Specialization (Coursera) • Published AI/ML researcher (Psychological Science, ICSE); 4 provisional patents in AI/computer vision 🌟 What Sets Me Apart: I am senior, and I still write the code. On every engagement you get one engineer doing the actual work: architecting, coding, testing, deploying, documenting. Because I have built AI in regulated healthcare and fintech environments, compliance, evals, and monitoring are baked in from day one rather than bolted on. My neuroscience background shapes how I build AI systems that genuinely understand human behavior and needs. 🤝 Working With Me: You work directly with me, and I personally do the work. Expect working code early (usually in the first week), frequent demos, clear async communication, and clean documentation at handover. US-based in San Diego (Pacific time), available for both short sprints and long-term builds. Have an AI feature or product that needs to get built? Send me the details and I will reply with exactly how I would build it.