Sina A.Private profile: Upwork shows its earnings only to signed-in members, so we check it every few weeks, not daily. MRR here is a rough estimate (Upwork's six-month earnings ÷ months active). Last read Oct 14, 2026.

I connect state-of-the-art AI models to real-world business data, production applications and automated workflows. The biggest challenge companies face isn't accessing LLMs: it's getting AI to securely, accurately and dynamically interact with corporate infrastructure. As an AI Integration Engineer, I bridge this exact gap. I focus on implementing highly accurate RAG (Retrieval-Augmented Generation) systems, designing structured AI workflows and building autonomous agents that manipulate business tools rather than just outputting text. By combining advanced data engineering, enterprise-ready API orchestration and low-latency vector infrastructure, I deliver production-ready AI applications that directly optimize operational costs. ### 🛠️ Core Technical Specializations 🛠️ ### • AI Integration & Workflow Orchestration (+178% Growth) Connecting foundational LLMs (OpenAI, Anthropic, Mistral) directly to business software, CRMs, and databases. Expert in building event-driven backend systems (Python, Node.js), managing continuous runtime scripts, and setting up intelligent low-code orchestrations (n8n, Make, Zapier). • Intelligent Data Extraction & Annotation (+154% Growth) Automating massive-scale ingestion pipelines to extract high-value unstructured web data. Specializing in scraping past complex anti-bot walls (using Playwright and Puppeteer) to cleanly structure, clean, and format raw information for LLM fine-tuning or model evaluation context. • Advanced RAG Pipelines & AI Chatbot Development (+71% Growth) Architecting low-latency Retrieval-Augmented Generation systems. Full implementation of advanced multi-format document chunking, automated metadata tagging, embedding generation and high-performance vector database orchestration (Pinecone, ChromaDB, PGVector). • Secure Backend Infrastructure & DevOps Deploying containerized AI workloads (Docker), managing cloud hosting systems and establishing secure networking layers (Zero-Trust, encrypted mesh routing) to ensure enterprise proprietary data remains private and protected during AI processing.