Python & JavaScript Full-Stack Engineer specializing in LLM Applications, FastAPI, Next.js, Web Scraping & Automation ⭐ Top Rated with 100% Job Success Score on Upwork ✅ 5+ Years of Full-Stack & AI Engineering | 50+ Projects Delivered I personally handle all development and client communication, ensuring high-quality, scalable, and production-ready systems without outsourcing critical work. From AI-powered MVPs to full-stack platforms, I take products from idea to deployed: pipeline, backend, and frontend, without needing a second developer to finish the job. I build full-stack applications in both Python and JavaScript. On the Python side, I work with FastAPI and Django; on the JavaScript side, with Node.js, Express, React, and Next.js. I specialize in building intelligent systems with LLMs, RAG pipelines, and multi-agent orchestration, plus web scraping and automation systems that run reliably at scale. I lead a small development team serving founders and startups, while also working individually with clients to deliver clean, scalable solutions. Most of my work has been on production systems serving real users, where I acted as the technical owner, not just an implementer. 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀: • RAG Pipelines with Hybrid Retrieval: Built BM25 + vector search systems with Pydantic v2 structured output and RAGAS evaluation frameworks. Most teams skip evals entirely; I don't. • LangGraph Multi-Agent Systems: Designed stateful handoffs, tool-calling chains, and self-hosted LLM deployments via Ollama for data-sensitive environments. • Document Intelligence Pipelines: Delivered structured extraction for legal, real estate, and compliance workflows where a hallucinated field is a real business problem. • Large-Scale Scraping Systems: Built parallel crawlers extracting millions of records from government portals, e-commerce platforms, and protected sites with CAPTCHA bypass and proxy rotation. • Web Automation Workflows: Automated form submissions, data entry, monitoring, browser tasks, and multi-step business processes, saving clients hundreds of manual hours. • Full-Stack Applications in Python & JavaScript: Shipped complete products with React/Next.js frontends and FastAPI, Django, or Node.js backends, Dockerized and deployed to AWS and Azure. I help startups and founders turn ideas into production-ready software. From rapid MVPs to AI-powered platforms and hands-free automation, I combine deep technical expertise with end-to-end ownership. I scope, estimate, build, document, and stay on until it's live in production. My 100% Job Success Score comes from not disappearing after the handoff. 𝗖𝗼𝗿𝗲 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: • Python Backend: FastAPI, Django, REST APIs, async programming, microservices • JavaScript Full Stack: Node.js, Express, React, Next.js, Tailwind CSS, TypeScript • AI/LLM Engineering: Anthropic API, OpenAI API, LangChain, LangGraph, Ollama, RAG, agent orchestration, Pydantic v2, RAGAS • Web Scraping & Automation: Playwright, Scrapy, Selenium, Puppeteer, CAPTCHA bypass, proxy rotation, parallel crawling, browser automation, scheduled bots, workflow automation • Databases & Vector Stores: PostgreSQL, MongoDB, FAISS, pgvector, ChromaDB • Cloud & DevOps: AWS, Azure, Docker, GitHub Actions, CI/CD automation 𝗪𝗵𝗮𝘁 𝗜 𝗗𝗲𝗹𝗶𝘃𝗲𝗿: ✔️ AI agents & LLM-powered applications ready for production ✔️ Full-stack applications in Python or JavaScript, frontend to backend ✔️ RAG pipelines with measurable retrieval quality ✔️ Web scraping systems extracting data at scale ✔️ Web automation for repetitive tasks, browser workflows & business processes ✔️ Clear communication, honest scoping, and support until deployment 𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀: Python Developer | JavaScript Developer | Full Stack Developer | Node.js Developer | AI Engineer | LLM Developer | LangChain | LangGraph | RAG Pipeline | FastAPI Developer | Next.js Developer | React Developer | Web Scraping Expert | Web Automation | Browser Automation | Bot Development | Playwright | Puppeteer | Scrapy | Selenium | Chatbot Developer | AI Agents | Backend Engineer | Docker | AWS | Anthropic Claude | OpenAI | Vector Database | Document Extraction

I build production AI pipelines that turn messy documents into structured data. 8 years in Python and data engineering. I specialize in document processing systems — the kind where you throw in a stack of scanned PDFs and get clean, validated JSON out the other side. ## What I actually build ### LLM-Powered Extraction Pipelines Multi-agent systems using Claude, GPT-4, and GPT-4o with structured output enforcement (Instructor, Pydantic). I design domain-specific agents that classify documents, extract fields, validate data, and reconcile conflicts across sources. Not prompt-and-pray — schema-enforced, retry-aware, production-grade. ### OCR & PDF Processing Amazon Textract (async API), PyMuPDF, PDFPlumber, Camelot, Azure Document Intelligence. I handle the real-world mess — scanned documents with poor quality, mixed orientations, concatenated multi-document PDFs that need splitting and boundary detection. Table extraction, form parsing, layout-aware chunking. ### Serverless & Event-Driven Architecture AWS Lambda, SNS, SQS, Step Functions, EventBridge, ECS/Fargate. I build pipelines where S3 uploads trigger processing chains — fan-out to parallel extractors, dead-letter queues for failures, CloudWatch monitoring for quality metrics. ### Healthcare Document Systems Currently running a production pipeline that processes medical records daily — licenses, clinical notes, immunization records, provider credentialing documents. Full pipeline: PDF ingestion → OCR → document classification → LLM extraction → deduplication → normalization → structured output. Built with HIPAA considerations. ### Legal Document Systems Production pipeline for UK cost-assessment workflows — ingests multi-document bill bundles (PDFs, scans, Excel ledgers, Outlook .msg), classifies them, extracts disbursements, receipts, invoices, and party data, reconciles figures across sources with a 5-case matching algorithm, and generates court-compliant inter-partes narratives. Every extracted figure traces back to its source page for audit defensibility. ### Speech-to-Text Pipeline - AWS Transcribe Medical (streaming WebSocket + batch S3 jobs) with HIPAA-compliant configuration - Deepgram integration as an alternate ASR backend - Speaker diarization (up to 30 speakers), stereo channel separation, custom medical vocabularies, specialty-specific vocab lists (e.g. orthopedics) - Multi-threshold confidence analysis for dot-phrase and quick-text detection ### LLM Clinical Note Generation - Instructor + Pydantic schema-enforced structured outputs (Claude / GPT-4 / Gemini) - Prompt Studio: a template-authoring system where clinicians design reusable note templates with variables, segment bindings, and inline citations back to the raw transcript - Two output modes — Ambient (multi-category SOAP-style synthesis) and Dictation (verbatim with cleanup) - DOCX template engine with inline placeholder tokens, dual-archive export, and anchor-based fill for exact-fidelity Word output ### AI Agent Orchestration LangChain, LangGraph, MCP servers, RAG systems, function calling. I build agents that do real work — not chatbots that summarize, but extraction systems that produce validated, structured data from unstructured sources. ## Anthropic API Cost Optimization I help teams cut Claude/LLM API spend in half on production deployments without sacrificing product quality. I work across the levers that actually move the bill: - **Prompt caching** — designed around real traffic patterns, not just toggled on - **Model tiering** — Sonnet where reasoning matters, Haiku everywhere else - **Context discipline** — lazy-loading tools, skills, and instructions instead of dumping everything upfront - **Tool-call efficiency** — fewer round-trips, smaller payloads I've done this on a medical-document extraction pipeline and a LangGraph trading agent — both running in production with real users and real budgets. If you've inherited an over-budget Claude deployment, I know where to look first. ## Tech I use daily Python, FastAPI, Anthropic/OpenAI/Gemini, Instructor, LangChain, LangGraph, Textract, PyMuPDF, PostgreSQL, Redis, S3, Lambda, SNS/SQS, Docker, Pydantic. 20+ projects delivered on Upwork. I'm strongest when the problem involves turning unstructured documents into clean, structured data at scale.

Enterprise AI Agent & RAG Architect. I help enterprise organizations eliminate LLM hallucinations, slash operational overhead, and scale intelligent document automation. I engineer custom, production-grade RAG pipelines and Intelligent AI OCR architectures that deliver deterministic data workflows with a verified 96.8% field-level accuracy across 10,000+ complex corporate pages, drastically reducing manual hours and software costs. If you are dealing with standard vector-search chatbots that hallucinate, lose context, or fail to read dense data tables, or if your legacy OCR tools are outputting broken, chaotic text from blurry faxes and invoices, I build custom-made, enterprise-grade software solutions to deliver clean, database-ready structured data. Unlike generalist script-writers who blindly dump data into basic vector stores, I design layout-aware, cost-optimized pipelines engineered specifically for highly confidential, unstructured, multi-column, and multi-page corporate assets. CORE WORKFLOWS & SEARCH-OPTIMIZED ARCHITECTURES: 1. Agentic production RAG Pipelines & Enterprise Knowledge Bases • Advanced Hybrid Search Indexing: Fusing dense semantic vector embeddings (Pinecone, Qdrant, ChromaDB) with sparse keyword retrieval (BM25) to guarantee specific alphanumeric codes, legal sections, and invoice serial numbers are never missed. • Two-Stage Context Reranking & Multi-Agent Orchestration: Implementing state-of-the-art orchestration layers (LangGraph, AutoGen) combined with Cross-Encoder Rerankers (Cohere Rerank, FlashRank) to filter retrieved document nodes down to the absolute best context chunks, slashing downstream token costs by up to 40% while accelerating execution speed. • Layout-Aware Hierarchical Chunking: Parsing structured document layouts natively to maintain strict parent-child context windows, preventing sentences or complex financial data tables from being blindly split in half during vector indexing. 2. Intelligent AI OCR & Multi-Modal Document Intelligence • Vision-Based Pre-Processing: Utilizing OpenCV, PaddleOCR, and LayoutLMv3 to automatically de-skew, binarize, and map out the multi-column reading order of messy scanned PDFs, faxes, or smartphone images before running extraction. • Multi-Modal LLM & Vision Data Extraction: Routing raw text tokens and visual patches through state-of-the-art vision-language models (GPT-4o, Claude 3.5 Sonnet, Llama-3.2-Vision, Qwen2.5-VL, and DeepSeek-VL2) to automatically repair transcription typos and misaligned characters by understanding the surrounding industry context. 3. Schema Enforcement & Human-in-the-Loop (HITL) Guardrails • Strict Schema Enforcement & Structured Outputs: Employing programmatic validation frameworks like Instructor and Pydantic to mathematically force LLM outputs into 100% compliant, database-ready JSON, CSV, or SQL structures at the native API level with strict JSON execution. • Algorithmic Triage: Tracking model confidence log-probabilities. High-confidence data saves instantly to production databases, while low-confidence edge cases route seamlessly to custom Streamlit dashboards for rapid human validation, ensuring minimal manual hours. PROVEN ENTERPRISE CASE STUDIES & SUCCESS METRICS: • Enterprise Document Intelligence: Multimodal AI OCR, Pydantic Extraction & Hybrid RAG Pipeline Designed and engineered an end-to-end production pipeline to automate the processing of dense, unstructured corporate assets (including scanned PDFs, financial statements, and high-volume faxes). Used LayoutLMv3 to preserve structural hierarchies and the Instructor library paired with strict Pydantic schemas to achieve an elite 96.8% verified field-level extraction accuracy across 10,000+ pages. • AI OCR Automation for High-Volume Faxes: Engineered a highly resilient extraction and contextual text-cleaning pipeline running flawlessly for complex, handwritten and blurry business records. • Markdown Data Extraction Specialist: Built a hyper-accurate pipeline converting complex document styles into structured markdown format for seamless vector indexing and RAG ingestion. • Enterprise Document Parsing: Consulted on and deployed custom backend AI microservices to clean, parse, and automate unstructured multi-page document flows. MODERN PRODUCTION TECH STACK: • LLMs & Vision-Language Models (VLM): GPT-4o, Claude 3.5 Sonnet, Llama-3.2-Vision, Qwen2.5-VL, DeepSeek-VL2, GenAI Software Development • Frameworks, Agentic Workflows & Validation: Instructor (Pydantic), LangChain, LlamaIndex, vLLM, Ollama, LangGraph, AI Agent Development • OCR & Computer Vision: Tesseract OCR, EasyOCR, PaddleOCR, OpenCV, LayoutLMv3, Marker, Text Extraction, Structured Outputs (JSON Enforcement), Intelligent OCR • Infrastructure & Vector DBs: Python, AI Chatbot Development, VectorDBs (Pinecone, Chroma, Qdrant), PostgreSQL, Docker, Data Scraping, Web Scraping, PDF to Excel Ready protect your data privacy, and with deterministic accuracy? Click "Invite to Job"

🏆 𝐄𝐱𝐩𝐞𝐫𝐭-𝐕𝐞𝐭𝐭𝐞𝐝 - 𝗧𝗼𝗽 𝟭% 𝗼𝗻 𝗨𝗽𝘄𝗼𝗿𝗸 ✅ 𝟕+ 𝐘𝐞𝐚𝐫𝐬 𝐨𝐟 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 ⏰ 𝟏𝟐𝟎𝟎𝟎 + 𝐔𝐩𝐰𝐨𝐫𝐤 𝐇𝐨𝐮𝐫𝐬 | 𝟏𝟎𝟎+ 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐞𝐝 ⭐️ $𝟕𝟎𝟎𝐤+ 𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐨𝐧 𝐔𝐩𝐰𝐫𝐨𝐤 I’m Abdul, an Upwork Certified AI/LLM engineer with 7+ years of experience helping startups and growing teams build smart AI solutions. From autonomous agents, MVPs, AI-native products to intelligent analytics and cloud-ready deployments, I will help scale your operations, workflows, and revenue. —------------------------------------------------------------------- ⭐️𝐓𝐫𝐮𝐬𝐭𝐞𝐝 𝐛𝐲 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐋𝐞𝐚𝐝𝐞𝐫𝐬⭐️ “Abdul helped us rethink our lead gen with AI-powered decision-making. His work with LLMs and pipelines was key.” — HS, Director of Data & ML, EZ Pack “Game-changer. Abdul brought serious AI expertise that moved the needle fast.” — Thomas E., CEO, Mission BI —------------------------------------------------------------------- 𝐂𝐨𝐫𝐞 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 & 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 ➡️ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 & 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐠𝐞𝐧𝐭𝐬 Autonomous LLM agents, domain-tuned business assistants, task automation, human-like conversational interfaces (OpenAI, Claude, open-source models) ➡️ 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) Hybrid semantic search + generation, document & knowledge-base Q&A, contextual accuracy tuning, vector-store architecture ➡️ 𝐋𝐋𝐌 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 & 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 Domain-specific fine-tuning (QLoRA), benchmark evaluation (perplexity, BLEU, custom QA metrics), reliability & guardrail testing ➡️ 𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 Python-based workflow automation, process orchestration, manual-work reduction, API & systems integration, n8n, make. ➡️ 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 Multi-agent data analysis, auto-generated dashboards & reports, structured + unstructured data pipelines, decision recommendations ➡️ 𝐂𝐥𝐨𝐮𝐝-𝐑𝐞𝐚𝐝𝐲 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 Production-scale deployment on AWS (Bedrock, SageMaker), GCP (Vertex AI), Azure, and Databricks —------------------------------------------------------------------- 💰 𝐊𝐞𝐲 𝐀𝐜𝐡𝐢𝐞𝐯𝐞𝐦𝐞𝐧𝐭𝐬 -Helped clients raise $3M+ through AI-powered growth. -Built 50+ AI systems, from multi-agent tools to insight engines. -Drove a 17% revenue increase in 7 months for a US startup. -Impacted 300,000+ users through data-led product strategies. -Graduated in the Top 5% in my Master’s degree class (specialization in AI) —------------------------------------------------------------------- 🛠️ 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 LLMs & Agents: GPT-4, Claude, LLaMA, Mistral, LangChain, LlamaIndex, MemGPT RAG Stack: FAISS, ChromaDB, Pinecone, Weaviate LLM Ops: Fine-tuning, Prompt Engineering, Evaluation Metrics, Guardrails Dev & Infra: Python, FastAPI, LangGraph, Streamlit, Chainlit, Flask Data Pipelines: PySpark, SQL, Event-Driven Workflows, Cloud Functions Cloud Platforms: AWS, GCP, Azure, Bedrock, SageMaker, Vertex AI, Databricks —------------------------------------------------------------------- I am available to understand your project needs and deliver the best solution efficiently. Click on the “Message” button and let’s have a chat —------------------------------------------------------------------- Keywords associated with my skill set: AI Agent Developer, AI Consultant, Chatbot Developer, AI Automation Expert, AI Integration Specialist, Machine Learning Engineer, LLM Developer, Generative AI, Agentic AI, Multi-Agent Systems, Large Language Models (LLMs), GPT-4, Claude, LLaMA, Mistral, LangChain, LlamaIndex, LangGraph, Retrieval-Augmented Generation (RAG), Semantic Search, Knowledge Retrieval, Vector Databases, FAISS, ChromaDB, Pinecone, Weaviate, Custom Embeddings, Memory Layers, Prompt Engineering, LLM Fine-Tuning, LLM Evaluation, MLOps, Model Deployment, AI Data Analysis, NLP, Python, FastAPI, Chainlit, Data Pipelines, PySpark, SQL, AWS, GCP, Azure, Bedrock, Vertex AI, SageMaker, Databricks, Cloud Functions, API Integration, Open Source Models, Healthcare AI Engineer, Healthtech, Edutech, Fintech.

I'm an AI engineer specializing in AI agent development, RAG systems, generative AI, and LLM automation — $90K+ earned on Upwork, 27 projects delivered, Top Rated, with production AI and enterprise software running for clients across healthcare, SaaS, and compliance-heavy industries. Most clients come to me after an AI prototype that never made it to production. I build AI systems that hold up under real users, real data, and real compliance requirements — not demos. 🤖 AI Agent Development Multi-agent systems and autonomous workflows using LangGraph, CrewAI, AutoGen, and custom agent architectures. Agents that make decisions, call your APIs, process documents, and complete real business tasks end to end. 📚RAG Systems & Enterprise Document Intelligence Retrieval Augmented Generation pipelines over your documents and knowledge bases — semantic search, chat-with-your-data, and enterprise document intelligence. I've built RAG systems searching 100,000+ documents with sub-second latency using Qdrant, Pinecone, ChromaDB, and FAISS, including fully on-premise, air-gapped deployments for compliance-heavy organizations. 🧠 LLM Integration & AI App Development Production integration of OpenAI API (GPT-4/GPT-5), Anthropic Claude, Gemini, Llama, and open-source models — including hybrid model routing that sends simple tasks to fast, cheap models and complex reasoning to heavy ones, cutting inference costs without cutting quality. AI chatbots, document processing, classification, and AI features inside existing products. ⚙️ AI Automation & Workflows LLM-powered automation using n8n, Make, and Python — lead processing, data extraction, content pipelines, and internal tooling that removes manual work. If your team is copy-pasting between tools, I can probably automate it. ☁️Cloud Infrastructure & AI Security AWS (EC2, ECS, S3, Bedrock), GCP, Docker, Kubernetes — scalable deployments, GPU inference, and AI security done properly: encrypted data pipelines, on-premise and air-gapped LLM deployments, role-based access control, and audit trails that satisfy enterprise compliance. Recent work includes enterprise software builds like a document intelligence platform with on-premise LLMs and SAP integration, and a multi-agent healthcare SaaS platform aggregating data from 32 medical directories with LLM-powered analysis and content generation. How I work: ✅ Production-ready code with testing, documentation, and monitoring ✅ Fixed-price milestones or clearly scoped hourly engagements ✅ Clear async communication and regular progress demos ✅ Ongoing optimization after deployment — most clients retain me after the first build Stack: Python, FastAPI, LangChain, LangGraph, OpenAI API, Claude, Qdrant, Pinecone, PostgreSQL, Supabase, n8n, Make, AWS, GCP, Docker, Kubernetes, Custom Software Development, Highlevel Development, Claude Opus, Claude Fable, n8n custom deployment, Enterprise Software, AI Automation Message me with what you're trying to build or automate — I'll respond with specific questions about your use case, not a template pitch.

𝗦𝗼𝗺𝗲𝘄𝗵𝗲𝗿𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝗶𝘀 𝗱𝗼𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘁𝗵𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘆. Copying data. Sorting emails. Screening resumes. Writing the same responses. That work costs $30K–$300K/year in salaries of people doing things machines should handle. I build the AI system that makes it disappear. Production-grade, running 24/7, inside the tools your team already uses. I've done this for freight companies, legal platforms, recruitment firms, EdTech companies, and content teams. The results are below. ━━━━━━━━━━━━━━━━━━━━━━ 𝗥𝗲𝗰𝗲𝗻𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 ➞ 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗙𝗿𝗲𝗶𝗴𝗵𝘁 — AI email intelligence system + custom Outlook plugin • 15,000+ emails processed per day • Inbound response time → under 2 minutes • AI-assisted drafting built directly into Outlook via Office JS • Unified Salesforce + DAT + Microsoft Graph API ➞ 𝗟𝗲𝗴𝗮𝗹 𝗧𝗲𝗰𝗵 (𝗬𝗖-𝗯𝗮𝗰𝗸𝗲𝗱) — AI chatbot for a $1.5B+ debt resolution platform • 8× increase in cross-sell conversion • Revenue contribution: 0.2% → 2% (10× growth) • RAG-powered legal knowledge retrieval ➞ 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗺𝗲𝗻𝘁 — Natural-language CV search platform • 10,000+ CVs indexed • Candidate discovery: 30 to 60 min → under 10 seconds • 70–80% of manual screening eliminated ➞ 𝗘𝗱𝗧𝗲𝗰𝗵 — AI analytics engine • Chart generation: 30 min → under 30 seconds • 80–90% of repetitive analysis automated • Non-technical users, zero code needed ➞ 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 — AI content engine • 200–500 posts per day, fully automated • Creation time: 2 to 3 hours → under 2 minutes • 15–20× output with no added headcount ━━━━━━━━━━━━━━━━━━━━━━ 𝗦𝗸𝗶𝗹𝗹𝘀 & 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸 ✅ AI Agents, Multi-Agent Systems, Agentic AI, Autonomous AI, Workflow Automation, Business Process Automation ✅ RAG, Retrieval-Augmented Generation, Semantic Search, Vector Databases, Pinecone, Weaviate, FAISS, ChromaDB, Hybrid Search ✅ LangChain, LangGraph, LlamaIndex, Pydantic AI, Model Context Protocol (MCP) ✅ OpenAI API, GPT-4o, GPT-o3, Claude Sonnet, Claude Opus, Gemini 1.5 Pro, Gemini 2.0 Flash, Llama 3.1, DeepSeek-R1, Mistral ✅ Custom GPT, GPT Builder, Custom Instructions, Knowledge Base, OpenAI Actions ✅ Voice AI, Conversational AI, Retell AI, Speech-to-Text, Inbound Voice Agents ✅ Document Intelligence, Azure Document Intelligence, OCR, PDF Parsing, Table Extraction, Form Processing, Invoice Automation, Data Extraction ✅ Office JS, Outlook Plugin, Outlook Add-in, Microsoft 365 Add-in, Excel Add-in, Word Add-in ✅ Gmail Add-on, Google Workspace Extension, Apps Script, Google Apps Script ✅ Email Automation, Sales Automation, CRM Automation, Lead Qualification, Sales Operations ✅ FastAPI, Python, Node.js, Express.js, REST API, GraphQL ✅ React, Next.js, TypeScript, Tailwind CSS, Streamlit ✅ PostgreSQL, MongoDB, MySQL, Redis, Elasticsearch ✅ AWS, Azure, GCP, Docker, Kubernetes, CI/CD ✅ Salesforce, Microsoft Graph API, Slack, Microsoft Teams, Stripe ━━━━━━━━━━━━━━━━━━━━━━ 𝗪𝗵𝗮𝘁 𝘁𝗼 𝗲𝘅𝗽𝗲𝗰𝘁 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗺𝗲 → Fixed scope and price before any work starts, no surprises → You'll understand every decision being made → I own the project end to end, idea through launch → Top Rated Plus · Top 1% on Upwork · 5-star track record ━━━━━━━━━━━━━━━━━━━━━━ If you have a workflow that feels like it should already be automated, message me. Tell me where your team loses the most time and I'll tell you straight whether AI can fix it and what that looks like. No pitch. Just a real conversation.

Over 8 years we have helped startups and product companies build software that scales, from first prototype to Series B. More than 45 products shipped across Healthcare, LegalTech, SaaS, E-Commerce, and Transport. Our AI engineering team builds production systems, not research demos. We design agent architectures, RAG pipelines, and AI workflows using TypeScript (Mastra, Vercel AI SDK) and Python (LangGraph, Agno, Pydantic AI, LangChain). We work with Claude, OpenAI ChatGPT models, Gemini, and open-weight models (including GLM, Kimi, DeepSeek and many more), choosing based on task fit and cost constraints. We practice eval-driven development. Automated eval pipelines, LLM-as-judge scoring, quality gates, all of that runs before anything ships to production. We build MCP integrations and custom Agent Skills, implement structured outputs with guardrails, set up agent observability with tracing, and design context architectures so models get the right information at every step. We have delivered domain-specific AI assistants with RAG and ongoing eval monitoring, agentic tools with multi-step reasoning and tool use, legal document processing at 95% accuracy, AI data aggregation with automated validation, voice agent systems, and generative UI products. Our full-stack team supports AI products with solid web infrastructure: React, Next.js, Vue.js, TypeScript, Node.js, GraphQL, and REST APIs. We deploy on AWS, GCP, and Vercel, and work with PostgreSQL, Redis, and vector stores (Pinecone, Qdrant, pgvector). For mobile we use React Native and Flutter. Delivered results: RAG-powered support bot reduced resolution time by 70% and lifted NPS by 20%. AI event aggregation pipeline scaled B2B SaaS onboarding from 1 to 5 clients per month at 95% data quality. LLM document system cut legal research from days to hours. AI landing page builder matched agency-level output with zero designer involvement. First-year ROI across AI projects averages 3.2x. We have built AI systems for LegalTech (tax advisory, contract analysis), Healthcare (medication search with voice agents), Transportation (fleet management, real-time optimization), E-Commerce (content generation), SaaS platforms with AI-native features, and B2B products with intelligent data pipelines. Our clients are startups embedding AI into their product, and companies replacing manual workflows with intelligent automation. We cover the full cycle: discovery, architecture, development, eval pipeline setup, deployment, and ongoing optimization.

Got unstructured documents that need to become structured, queryable intelligence? I build the end-to-end AI pipelines that make that happen, reliably and at scale. With 5+ years of experience in AI-driven backend systems and knowledge engineering, I specialize in turning messy, real-world data into clean, ontology-aligned knowledge graphs using modern LLMs, graph databases, and cloud infrastructure. Here is what I bring to the table: Knowledge Graph Development — Design and maintenance of production-grade graphs on AWS Neptune and Neo4j, with ontology-driven data modeling and schema refinement as systems evolve LLM Extraction Pipelines — Multi-pass entity, event, and relationship extraction using structured prompting, Pydantic data modeling, and fine-tuned models for improving accuracy over time Batch Document Ingestion — Automated ingestion from public sources using web scraping tools like Crawl4AI, with robust text parsing and normalization for downstream ML tasks AWS AI & ML Infrastructure — Deploying and managing AI workloads on SageMaker and Bedrock with proper observability, versioning, and CI/CD practices Human-in-the-Loop Workflows — Building review cycles that let domain experts validate and correct extractions, feeding improvements back into the model over time Backend APIs & Services — Clean Python backends that expose the knowledge graph as fast, reliable, queryable APIs for downstream applications I have worked on technically deep systems where data quality, pipeline reliability, and schema consistency are not optional. I am comfortable owning a sophisticated backend from ingestion all the way to query layer, without needing constant direction. If you are building a system that turns documents into intelligence, I would love to be the engineer who makes it production-ready. Send me a message and let us get started.

I build production-grade ML systems and agentic automation infrastructure that transform raw data into real-time decisions, workflows, and execution systems. My work sits at the intersection of: * Machine learning systems (forecasting, prediction, optimization) * Agentic workflows (multi-step automation, decision agents) * Real-time data infrastructure (APIs, pipelines, monitoring systems) The outcome is simple: systems that don’t just analyze data — they act on it. What I Build 1. ML Forecasting & Predictive Systems Production-ready time-series and predictive pipelines: * Demand, revenue, and behavioral forecasting systems * Prophet and deep learning-based models * Automated retraining and versioned inference APIs * Real-time scoring for live production environments 2. Optimization & Decision Engines Turning constraints into executable systems: * MILP-based optimization (routing, allocation, scheduling) * Policy simulation and scenario modeling * Resource allocation and tiering engines * Decision automation tied to live data streams 3. Agentic Automation Systems (LLM + Workflow Agents) Autonomous multi-step systems that execute workflows: * Multi-agent orchestration pipelines * Workflow automation and decision simulation systems * Signal extraction → reasoning → action pipelines * Event-driven agents for monitoring and execution 4. Real-Time Data Infrastructure Production-grade data systems for high velocity environments: * FastAPI-based inference and orchestration layers * Event-driven pipelines (APIs, SFTP, streaming sources) * Time-series databases (TimescaleDB, etc.) * Monitoring, logging, and failure recovery systems 5. Financial & Risk Systems Applied ML for high-stakes decision environments: * Credit risk scoring and financial analytics pipelines * Trading signal generation and backtesting systems * P&L simulation and scenario modeling * Structured decision support systems Selected Production Work Real-Time Telecom Forecasting System Built a forecasting and matching engine processing 30,000+ users with 15-minute refresh cycles on Azure, replacing manual allocation logic with a production ML + API system. Multi-Agent Intelligence System (PulseAgent) Designed a multi-agent workflow system that orchestrates reasoning and execution across data sources for automated decision-making. Decision Intelligence Engine (Deal Intelligence Agent) Built a signal extraction and ranking system that converts raw data into actionable business intelligence using ML + agent workflows. Automated QA & Monitoring System Developed a production monitoring system for validating pipelines, detecting failures, and ensuring system reliability in real time. Credit & Financial Risk Automation System Engineered a financial analytics engine for credit evaluation, ratio modeling, and automated risk scoring How I Work I design systems with production reality in mind: * Every model is deployed behind an API or workflow engine * Every system includes observability and failure handling * Every output is traceable, testable, and reproducible * Every pipeline is built for real operational use, not demos If it doesn’t run reliably in production, it doesn’t ship. Who I Work With * Startups building AI-driven products * Fintech and analytics teams * Companies automating decision workflows * Teams moving from manual analysis → autonomous systems Let’s Work Together If you need systems for: * Forecasting and prediction at scale * Optimization and decision automation * Agentic workflows or AI-driven systems * Real-time ML infrastructure in production I can design and deploy the full system end-to-end.

Need a Claude/LLM integration, a quant trading system, or a financial data pipeline that survives contact with real data? I build Python backends that ship on time, ship with tests, and run in production — not scripts that break next week. Recent work: → Real-time market pattern detection for a US trading client — 125+ stock symbols, ClickHouse tick ingestion, live RSI and VWAP signal alerts via Pushover, pair trading backtests at Sharpe 2.38 and 66.7% win rate. Milestone delivered on time, signals confirmed in live production. → SEC EDGAR 10-K parser for Business Development Companies — multi-source Schedule of Investments extraction, 103 companies, $1,324,753K total reconciled to the audited Total Investments line, 58/58 tests passing, profile-driven architecture, Docker. What I build: → Claude/LLM integrations — Tool Use, prompt caching (51.8% hit rate in production), structured output, SSE streaming, FastAPI endpoints. Production-grade scope enforcement and cost control. → Quant & trading systems — Signal generation, multi-timeframe funnels (ADX, EMA, Bollinger, volume, ATR-based sizing), backtesting, exchange API integration (Binance, MT5), ClickHouse and SQLite tick storage, Telegram and Discord alerting. → Financial data engineering — SEC EDGAR filings, multi-source ETL across financial APIs (EODHD, Financial Modeling Prep), Pandas + PostgreSQL pipelines, idempotent ingestion with deduplication and audit logging. → ML & classification pipelines — fastText, scikit-learn, custom feature engineering, confidence calibration, FastAPI inference services with automated retrain workflows. → Backend services & bots — FastAPI with async REST, n8n workflow integration (cron + REST dispatch + status polling), Telegram and Discord bots, scheduled tasks, persistent state, error recovery. How I work: every project Dockerized, deployed on DigitalOcean (or your cloud), version-controlled, tested, documented. I respond within hours, ask scoping questions before quoting, and ship on time. If something breaks after delivery, I fix it. Bilingual EN/中文 native, Taiwan-based. Available 30+ hours per week. Comfortable with US morning hours.