Hire the Best BERT Specialists

I don't just wrap APIs. I build the algorithms underneath them. I'm an AI engineer with 8+ years across quantum-simulation ML, fintech, EdTech, and clinical research — and an MS in Theoretical Chemistry. I train transformers from scratch, invent production retrieval algorithms, and ship AI inside a $660M global Clinical Research Organization where "almost right" doesn't pass. If your last contractor handed you a LangChain demo and called it a product, we should talk. 🔹 What Sets Me Apart ✅ Inventor of the Fox-Search Algorithm — a hybrid vector + graph retrieval method I built for asymmetric query/target search (short questions against 200-page clinical protocols). Pairs a trainable ~0.3B-parameter BERT-tier model with FAISS where pure embedding search collapses. ✅ I train my own models — fine-tuning a 0.3B encoder is normal work for me, not outsourced to a Hugging Face checkpoint. ✅ Scientific rigor — career started predicting atomistic energies and forces for ab-initio-quality molecular simulations. Same measurable-accuracy discipline now applied to LLM systems. ✅ Founder of edup.ai — autonomous grading of handwritten STEM work: computer vision + handwriting recognition + math reasoning + rubric generation, end-to-end. ✅ Production AI in a regulated industry — currently building document intelligence at Worldwide Clinical Trials (2,900+ employees, 52 countries). 🔹 Core Capabilities ▪ RAG done right — hybrid vector+graph retrieval, FAISS at scale, embedding fine-tuning, evaluation harnesses that actually measure quality. If your RAG hallucinates, I can usually tell you why within an hour. ▪ LLM engineering — self-optimizing prompt primitives that improve over time, constrained generation, tool calling, structured outputs. GPT-4o, Claude, Llama, Mistral. ▪ Document intelligence — production parsers for PDF, DOCX, XLSX with section-aware XML processing; semantic search that finds alternative phrasings of previously-approved language. ▪ Custom model training — BERT-tier encoders, LoRA/QLoRA fine-tuning, domain-adaptive pretraining. ▪ Computer vision — handwriting recognition, OCR, ViT/CLIP, math symbol understanding. ▪ High-performance engineering — C++ on the hot path, FastAPI services, real async Python. 🔹 Tech Stack • Languages: Python, C++, JavaScript / TypeScript • ML / DL: PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, NumPy, Pandas • LLM: OpenAI GPT-4o / o-series, Claude, Llama, Mistral, LangChain, LangGraph, LlamaIndex, LoRA / QLoRA, RAG, function calling • Retrieval: FAISS, Pinecone, Weaviate, Chroma, Qdrant, custom hybrid vector+graph search • Vision: OpenCV, PyTorch Vision, ViT, CLIP, OCR, handwriting recognition • Backend: FastAPI, Flask, Docker, REST, WebSockets, async Python • Frontend: Svelte 4, React • Enterprise: Microsoft Entra ID, SharePoint, Azure, AWS, GCP 🔹 Shipped Projects ▪ Fox-Search RAG — hybrid vector+graph retrieval running in production for clinical document Q&A, outperforming pure embedding baselines on long-form documents. ▪ Self-optimizing prompt infrastructure — reusable LLM primitives that improve over time without manual prompt-tuning. ▪ Document intelligence backend — parses Excel/DOCX/PDF with section-level XML comprehension; powers cross-document semantic search. ▪ RFI automation for clinical trials — ingests Excel-based Requests for Information and routes them through automated answer pipelines. ▪ edup.ai — 7-stage autonomous grading pipeline for handwritten math, from photo capture to gradebook. Live at edup.ai. ▪ Neural network potentials — ML approximations of quantum-mechanical energy surfaces for molecular simulation (same problem class as DeepMind's AlphaFold lineage). 🔹 How I Work ✔ I scope honestly — if your problem doesn't need AI, I'll tell you. ✔ I write the evaluation code before the model code. Anyone can demo; I deliver something you can measure. ✔ I ship in weekly increments — no six-week silence followed by a "big reveal." ✔ I write maintainable code your team can own after I'm gone. 🔹 Best Fit For Teams stuck on "our RAG kinda works but hallucinates" • Document-heavy domains (clinical, legal, scientific, financial, EdTech) • Startups needing a senior engineer who can architect, train, deploy, and operate • Anyone whose roadmap requires training models, not just prompting them. Send me your problem — not your solution. I'll reply within 24 hours with an honest read on whether I'm the right engineer for it.

𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝗱𝗼𝗻'𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗿𝗲𝗱𝘂𝗰𝗲 𝗺𝗮𝗻𝘂𝗮𝗹 𝘄𝗼𝗿𝗸, 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲𝗶𝗿 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝗵𝗲𝗹𝗽 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼 𝗺𝗼𝗿𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗵𝗶𝗿𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗽𝗲𝗼𝗽𝗹𝗲. I'm an AI Systems Architect and OpenClaw Specialist with 3+ years of experience building AI agents, business automation systems, RAG solutions, and full-stack AI applications that run reliably in production. 𝗪𝗵𝗮𝘁 𝗜 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵: - AI Agents and Task Automation - Business Workflow Automation - OpenClaw Setup and Custom Development - RAG and Internal Knowledge Systems - Full-Stack AI Applications - API Integrations and System Connectivity - Internal Tools and Operational Dashboards - MCP Servers and Custom Tool Integrations 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 𝗜 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵: - E-commerce and SaaS my main focus - Also work with agencies, logistics, real estate, and more 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗺𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁: - I focus on solving real business problems, not adding unnecessary complexity - I take projects from planning all the way to deployment - I build things that teams can actually use every day - I hold relevant certifications in AI and cloud technologies - You can see my work in action live demos available on request 𝗥𝗲𝗰𝗲𝗻𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀: - Built AI agents that helped teams handle the workload of 3-5 extra people without expanding the team - Developed OpenClaw assistants that use tools, remember context, and complete tasks across workflows - Created internal document and knowledge search systems - Built full AI applications from planning and design to live deployment - Connected AI systems with APIs, databases, and existing business software 𝗖𝗼𝗿𝗲 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀: OpenClaw • Python • FastAPI • LangChain • LangGraph • RAG • PostgreSQL • React • Next.js • Node.js • TypeScript • Docker • OpenAI • Claude • MCP 𝗟𝗲𝘁'𝘀 𝗧𝗮𝗹𝗸: If you want to automate your operations, reduce manual work, or build an AI-powered system , send me a message and let's get it built.

I build and ship production AI systems that real users depend on, not demos. RAG pipelines, multi-agent LLM apps, fine-tuned models, and multimodal/OCR extraction, deployed to run 24/7 on Kubernetes and serverless GPU. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Top-Rated Plus | 100% Job Success | 4+ years ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Enterprise-grade AI for multinational companies and startups, including HIPAA-conscious healthcare workflows. I turn complex requirements into intelligent, production-ready applications that drive measurable results. ━━━━━━━━━━━━━━━━━━━━━ WHAT I DO BEST ━━━━━━━━━━━━━━━━━━━━━ Agentic AI & Multi-Agent Systems Custom architectures with LangGraph, CrewAI, and Model Context Protocol (MCP), including self-improving agents that learn from evaluation feedback. Built for real automation, not chatbot demos. Advanced RAG, Evaluation & Observability 10+ production RAG systems (self-RAG, adaptive retrieval), one serving hundreds of users across thousands of documents. Migrated Pinecone to Weaviate for better recall at lower cost. Every system ships with LLM-as-judge, retrieval metrics, and full tracing (LangSmith/Langfuse), so quality is measured, not guessed. LLM Fine-Tuning & Cost Optimization PEFT (LoRA/QLoRA), SFT, DPO, and instruction tuning. Fine-tuned a 7B Arabic model served on autoscaling serverless GPU, plus multimodal vision-language models. Cut client AI costs by up to 40% through open-source replacement and quantization, with no drop in performance. Multimodal & Document AI OCR and document-extraction pipelines across PDF, DOCX, PPTX, Excel, and images, with strong F1 on messy financial and clinical documents. Also built a temporal, multi-hop knowledge graph over an encrypted Postgres + Qdrant store with client-side encryption. AI Automation & Integrations Connecting LLMs to real business systems: n8n, Make (Integromat), Zapier, CRM automation (HubSpot, GoHighLevel, Airtable), Supabase backends, and Twilio/WhatsApp. AI that plugs into how your team actually works. Enterprise Backend & Scalable Infra Master-level Python (FastAPI, Flask), robust CI/CD, and multi-cloud deployment (AWS, Azure, GCP). Docker + Kubernetes with KEDA autoscaling, plus privacy-first, multi-tenant systems (E2EE, RBAC, audit logging), including HIPAA-conscious PHI handling. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ TECH STACK ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ Agents & LLMs: LangChain, LlamaIndex, LangGraph, CrewAI, MCP, Hugging Face (PEFT/TRL), Ollama, TGI, vLLM ▸ Eval & Tracing: LangSmith, Langfuse, LLM-as-judge, custom eval frameworks ▸ Vector DBs: Weaviate, Pinecone, Qdrant, FAISS, ChromaDB ▸ Models: OpenAI, Claude, Gemini, fine-tuned open-source ▸ Automation: n8n, Make (Integromat), Zapier, Supabase, Twilio ▸ Backend: Python (FastAPI, Flask), TypeScript/Node (NestJS, NextJS), PostgreSQL, MongoDB ▸ MLOps & Cloud: Docker, Kubernetes, KEDA, CI/CD, Airflow, MLflow; AWS (SageMaker, Lambda), Azure ML / Azure OpenAI, GCP, serverless GPU ▸ CV & Data: OCR optimization, vision-language models, Stable Diffusion, web scraping ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WHY CLIENTS PICK ME ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ Ships to production. I build AND deploy. You get systems that run 24/7 and scale, not a prototype someone else has to finish. ▸ Proven track record. Top-Rated Plus, 100% Job Success, enterprise and healthcare AI delivered end-to-end. ▸ Innovation-driven. I bring the latest (MCP, adaptive RAG, new model releases) into production. ▸ Cost-conscious. High-performance AI that optimizes spend without compromising quality. ▸ Quality-first. Production-grade code, proper testing, and evaluation built in. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Building an AI product, or need one taken from prototype to production and scaled reliably? Send me the brief and I'll tell you exactly how I'd approach it.

I help teams evaluate, test, and improve AI systems across model outputs, tool-use workflows, voice agents, automation pipelines, and multimodal tasks. My strongest fit is AI training and evaluation work: LLM evaluation, rubric-based QA, data annotation review, prompt and response quality checks, AI red teaming, prompt-injection testing, web/browser task evaluation, and workflow correctness review. I create realistic task prompts, identify hallucinated or incomplete tool behavior, document failure patterns, and turn messy AI outputs into clear evaluation notes. I also build the systems around that work. I use Python, n8n, Make, Zapier, OpenAI API, Claude Code, OpenClaw, Vapi, Retell, ElevenLabs, Google Sheets, and GitHub to build practical automations, voice-agent QA flows, lead workflows, reporting systems, and proof-of-work projects. Relevant proof: - 100% Job Success and Rising Talent on Upwork. - Completed OpenClaw/Ollama local AI agent setup work with 5.0 client feedback. - Built AI voice-agent and appointment-booking workflows. - Built GitHub-backed AI evaluation, red-team, annotation QA, and workflow automation projects. - GitHub is linked in my Upwork linked accounts. If you need someone who can evaluate AI outputs carefully, follow rubrics, write clear feedback, test real workflows, and also understand the automation stack behind modern AI agents, I can help.

Availability: Full-time freelancer, 𝟰𝟬+ hours/week, open to long-term collaborations. I’m a Full-Stack & AI Engineer with 10+ years of experience building web and mobile applications and 3+ years of specialized experience in AI and Large Language Models (LLMs). I design, develop, and deploy production-grade platforms, from scalable SaaS dashboards to AI-powered assistants, RAG systems, and voice agents. I work end-to-end: architecture → backend → frontend → cloud deployment, with a focus on clean code, maintainable systems, and high performance. Over the past few years, I’ve delivered solutions that integrate AI/LLM pipelines, vector search, real-time chat, and voice agents for enterprise and startup clients. 🤖 AI & LLM Expertise - MCP Server Development: Designing and integrating custom MCP servers for AI agents, enabling structured tool usage, external system integrations, database querying, and API orchestration. - Fine-Tuning: Persona creation, Q&A systems, and domain-specific models (medical, legal) using Mistral and Llama 3. - Synthetic Dataset Generation: Streamlining LLM training with high-quality datasets. - Evaluation Frameworks: Assessing LLM performance with custom metrics. - Cloud Deployment: Deploying LLMs on AWS and GCP. - AI Agents & Voice Bots: Proficient with LiveKit, Retail AI, OpenAI. - Open-Source Deployment: Expertise deploying models like vLLM on AWS/GCP/RunPod using SkyPilot. 🛠️ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗧𝗼𝗼𝗹𝘀 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 ➜ LLM Tools: LangChain, Langsmith, Langfuse , Hugging Face, Transformers. ➜ Vector Databases: Chroma, FAISS, Pinecone, Qdrant , Opensearch ➜ AI Workflows: Flowise AI, LangFlow, StackAI. 🛠️ 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 ➜ Languages & Frameworks: Python, Node.js, ReactJS. ➜ Database Management: MongoDB, MySQL, PostgreSQL , Supabase , FIrebase ➜ Frontend & Backend Integration: Seamlessly connecting APIs and user interfaces. 🌟 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 ➜ Open-Source LLMs: Proficiency in LLAMA 3, Mistral 7B, and Mixtral 8x7B. ➜ Prompt Engineering: Expertise in techniques like Chain of Thought, Few-shot Prompting, and Self-Reflection. ➜ Fast Inference: Implementing high-speed solutions with vLLM . 🌟 𝗪𝗵𝘆 𝗖𝗵𝗼𝗼𝘀𝗲 𝗠𝗲? With over 10 years of experience, I deliver scalable, cutting-edge solutions tailored to your project’s needs. Whether it's advanced AI models, MCP server development, LLM optimization, or full-stack development, I ensure top-notch results every time. Let’s collaborate to bring your ideas to life!

⭐⭐⭐⭐⭐ Senior AI Engineer | Production 𝐑𝐀𝐆, 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡 | 𝐏𝐲𝐭𝐡𝐨𝐧, 𝐀𝐖𝐒, 𝐂𝐥𝐚𝐮𝐝𝐞 I'm a AI Systems Engineer and Senior Python Backend Engineer with 8+ years of backend development experience, specializing in LangGraph, advanced multi-agent workflows, and production-grade Agentic RAG. I help business unlock the value of their private data by building custom AI systems that are accurate, secure, resilient, and production-ready. Unlike generic ChatGPT wrappers, my solutions ground frontier LLMs like Anthropic Claude and OpenAI in your own documents and databases - eliminating hallucinations, enabling complex agentic reasoning, and ensuring answers are traceable to source material. I've delivered robust RAG pipelines for legal tech, healthcare, e-commerce, and internal enterprise knowledge bases. 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐝 - 𝐄𝐧𝐝‑𝐭𝐨‑𝐄𝐧𝐝 𝐑𝐀𝐆 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 ➤ Core stack: Python + LangGraph / LangChain / LlamaIndex + Vector DB (Pinecone, ChromaDB, FAISS) + OpenAI / Claude + AWS (Bedrock, Lambda, S3, ECS) ➤ Typical project scope includes: ○ Multi-Agent Workflows: Build complex, stateful, and cyclical LLM applications using LangGraph for advanced decision-making and tool use. ○ Ingestion pipeline: Parse PDFs, Word docs, HTML, markdown, Slack exports, Confluence, Notion, or database dumps. ○ Smart chunking & embedding: Semantic chunking, overlap strategies, and embedding with text-embedding-3, Cohere, or open‑source BGE models. ○ Hybrid retrieval: Combine vector similarity (dense) with BM25 keyword search (sparse) + cross‑encoder re‑ranking for maximum precision. ○ Generation with source grounding: Prompt engineering to force citations, confidence scores, or fallback responses when data is insufficient. ○ Conversational memory: Chat history summarization, session management, and multi‑turn RAG for assistants that remember context. ○ Caching & optimization: Semantic caching (GPTCache), embedding caching, and async retrieval to reduce latency and token costs. ○ Evaluation & observability: RAGAS, TruLens, or custom metrics - context relevancy, answer faithfulness, recall, and latency tracking. 𝐀𝐈 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫 e & 𝐀𝐖𝐒 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 - 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐑𝐞𝐚𝐝𝐲 ➤ I don't just write scripts - I build secure, scalable, serverless or containerized backends. Every AI system I deliver is: ○ Containerized with Docker - Reproducible environments. ○ Deployed on AWS - Using API Gateway + Lambda (serverless inference), ECS/Fargate (for long‑running jobs), S3 (document storage), RDS/DynamoDB (metadata), and Bedrock (for enterprise-grade cloud LLMs like Claude). ○ Secure by design - API keys managed via Secrets Manager, IAM roles, VPC isolation, and rate limiting. ○ CI/CD ready - GitHub Actions or GitLab CI for automated testing and deployment. ○ Monitored - CloudWatch logs, X‑Ray tracing, and custom dashboards for usage, latency, and cost tracking. ➤ Example architectures I've delivered: ○ Agentic Customer Support System - Built with LangGraph, utilizing custom tool-calling for CRM integration and automated ticket resolution. ○ Chat‑over‑PDFs for a law firm - 10,000+ documents, sub‑second retrieval, Claude 3.5 Sonnet + Pinecone serverless on AWS Lambda. ○ Semantic product search for an e‑commerce store - hybrid search (FAISS + BM25) with re‑ranking, deployed on ECS. ○ Internal company FAQ bot - Slack‑integrated, using ChromaDB and OpenAI, with conversation memory and admin feedback loop. 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐓𝐨𝐨𝐥𝐛𝐨𝐱 (𝐧𝐨 𝐭𝐚𝐛𝐥𝐞𝐬 - 𝐜𝐥𝐞𝐚𝐧 𝐥𝐢𝐬𝐭𝐬) ○ Languages & Frameworks: Python (advanced) | TypeScript (basic) | FastAPI | Django REST | Flask | GraphQL ○ LLM & RAG Frameworks: LangGraph | LangChain | LlamaIndex ○ Vector Databases: Pinecone | ChromaDB | FAISS | Qdrant | Weaviate | Milvus ○ LLM Providers & Models: Anthropic Claude | OpenAI | Google Gemini | Groq | Together.ai | LLaMA 3 | Mistral | Mixtral | Zephyr (via Ollama, vLLM, HuggingFace) ○ Embedding Models: OpenAI Ada | text‑embedding‑3‑small/large | Cohere | Voyage | BGE | Instructor ○ AWS Services (primary cloud): Lambda | API Gateway | ECS/Fargate | S3 | Bedrock | RDS (PostgreSQL with pgvector) | DynamoDB | Secrets Manager | CloudFront | CloudWatch ○ Other Cloud Options: Google Cloud (Vertex AI, Cloud Run) | Azure (AI Search, OpenAI) ○ Databases & Caching: PostgreSQL (pgvector) | DynamoDB | Redis | GPTCache ○ DevOps & Testing: Docker | GitHub Actions | Terraform (basic) | pytest | RAGAS | TruLens | DeepEval | LangSmith ○ UI for Demos: Streamlit | Gradio | Chainlit 𝐋𝐞𝐭'𝐬 𝐖𝐨𝐫𝐤 𝐓𝐨𝐠𝐞𝐭𝐡𝐞𝐫 If you need a reliable, backend-first, AWS‑savvy AI Systems Engineer who writes clean Python and delivers real business value, let's talk.