Hire the Best memoQ Specialists

Looking for someone who communicates professionally, stays organized, and learns quickly? I am an English Teacher and English Coordinator with extensive experience in communication, organization, and client-focused support. My professional background has strengthened my ability to manage conversations, solve problems, coordinate tasks, and maintain positive relationships in fast-paced environments. I recently completed professional training in telemarketing and client communication, where I learned the fundamentals of lead generation, CRM systems, and structured sales communication. I am eager to apply these skills in a real business environment and continue developing professionally. My services include: • Customer support (Email & Chat) • Virtual Assistance • Lead Generation • LinkedIn Outreach • CRM data entry and organization • Calendar and administrative support • English-Arabic communication Why work with me? ✔ Fluent in English and Arabic ✔ Strong written and verbal communication ✔ Highly organized and detail-oriented ✔ Fast learner who adapts quickly ✔ Professional, reliable, and committed to delivering quality work I am currently available full-time and ready to contribute to your team.

I build production-grade GenAI systems for Fortune 500 enterprises including Verizon and PNC, : AI agents, RAG platforms, semantic layers, knowledge graphs, text-to-SQL/NLQ systems, and full-stack AI applications. I have 9+ years of experience across machine learning, computer vision, NLP, backend engineering, and full-stack product development. Recently, I have led large engineering teams building enterprise AI platforms for telecom and banking use cases. What I can help you build: • AI agent systems using LangGraph, LangChain, MCP, A2A, FastAPI, and custom tool orchestration • RAG applications over documents, databases, knowledge bases, and enterprise data • Semantic layers and ontology-driven systems for analytics and GenAI use cases • Text-to-SQL / natural language database querying over Postgres, BigQuery, and Teradata • Knowledge graph RAG using Neo4j, vector databases, metadata extraction, and domain modeling • Full-stack AI SaaS products with Python, React, Next.js, Docker, Kubernetes, and cloud deployment • LLM fine-tuning, evaluation, prompt engineering, retrieval evaluation, and hallucination reduction Relevant achievements: • Led a 14-member team to build an asynchronous, extensible, A2A-compliant agentic framework for enterprise troubleshooting. • Reduced ticket resolution time in a production troubleshooting workflow from days to minutes. • Built and deployed hundreds of tools across multiple MCP servers. • Led a 12-member team to build a natural language query platform for enterprise databases. • Improved NLQ accuracy using domain modeling, metadata generation, and knowledge graph traversal. • Built an ontology-powered Knowledge Graph RAG platform over unstructured enterprise data. • Co-founded and scaled an AI video translation/dubbing product from $0 to $100k revenue in 6 months. • Built computer vision systems deployed across industrial locations in India and Europe. Tech stack: Python, FastAPI, LangChain, LangGraph, LlamaIndex, OpenAI, HuggingFace, PyTorch, PEFT/LoRA, React, Next.js, Postgres, BigQuery, Neo4j, Pinecone, Docker, Kubernetes, ArgoCD, GCP, AWS, PySpark, Airflow. I am best suited for clients who need more than a prototype. I can help you go from ambiguous AI idea → system design → implementation → deployment → evaluation → production hardening.

Hardworking and Reliable Document Controller with 6+ Years of Experience I am a dedicated professional with over six years of experience in document control, consistently meeting and exceeding client expectations. I possess strong expertise in managing and organizing technical documentation, with hands-on experience using platforms such as Aconex (Oracle/Primavera), SharePoint, Outlook, Teams and Coreworx. In addition to document control, I have solid skills in data management, data entry, and investor-related tasks, ensuring accuracy, confidentiality, thorough data validation, and efficiency in every project I handle. I am also proficient in email organization and management, creating and editing fillable PDF forms, and transforming surveys into clear, professional formats. Furthermore, I bring creative support skills, including editing reels and TikTok videos using CapCut and Final Cut Pro, as well as designing publication materials (pubmats) and social media content using Canva—skills I have developed and practiced since my college years.

Hi, I’m the manager of TranslateFuture, an Arabic translation studio with 10 years of hands-on experience. We are a complete team of specialists who treat every word as a responsibility, not just a task. At TranslateFuture, we do more than assign translators. We pair each project with subject-matter experts to guarantee accuracy and trustworthiness. Our network includes a legal specialist, a physician, an engineer, and a seasoned trading professional. We also work with designers and developers, so your files arrive clean, consistent, and ready to use whether they live in HTML, JSON, subtitle formats, or standard documents. What we do - Expert Arabic translation and localization for specialized content, with a strong track record in crypto, forex, and trading - End-to-end handling of formats and layout so you receive final, publication-ready files - Work with all major CAT tools (Smartcat, Trados, MemoQ (Phrase), Smartling, Lark) and video translation workflows (CaptionHub) - Available 7 days a week for urgent and time-sensitive projects How we work - No literal, word-for-word output. We research every term and nuance. - We understand how Arabic is spoken and written across 22 countries and adapt tone, register, and phrasing accordingly. - When clarity helps the reader, we add concise notes backed by reliable references. Our goal is Arabic that reads as if it were originally written in Arabic while preserving the exact meaning and marketing impact of the source. We stand behind every translation we deliver and stay close to you through each step until you are fully satisfied. Tell us about your project, and let’s craft Arabic content that informs clearly and resonates naturally.

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 if it's the right approach and how I'd build it.

I've spent 5+ years building production AI systems — chatbots, voice agents, RAG pipelines, multi-agent workflows, and full-stack AI tools that solve real business problems, not just pass a demo. I don't call an API and hand you a script. I think through the full system — architecture, reliability, edge cases, and the user on the other end — and I build something that actually holds up. 📦 Recent Projects → 🎭 new varioum — Shakespeare RAG system. Ask anything about Shakespeare's works and get accurate, source-grounded answers powered by a custom RAG pipeline → 🎙️ Voice AI Agent — Real-time voice agent with cloned voice output. Sub-2s response. Flask + ElevenLabs + Whisper → 👕 Virtual Try-On (Clothing) — AI-powered virtual fitting room that lets shoppers see garments on themselves in real time. Pose detection + garment warping for realistic fit and draping, built with computer vision and diffusion-based image generation → 🌍 Perplexity-Style Chatbot — Live web search + RAG pipeline for real-time, source-grounded answers → 🔗 Lead Automation Pipeline — End-to-end workflow across Notion and Google Sheets. Zero manual data entry → 📂 Smart File Analyzer — Upload any doc, get instant summaries, visualizations, and structured insights 🛠️ What I Build 🤖 AI Agents & Chatbots — Multi-agent systems and LLM-powered assistants using Claude, GPT, and Mistral. Context-aware, reliable, production-ready 🧠 RAG & Knowledge Systems — Connect your docs, databases, and internal data to an LLM that actually retrieves accurately. Pinecone, Qdrant, ChromaDB, FAISS 🎙️ Voice AI — Real-time voice agents with cloned voices, live transcription, and low-latency response for customer-facing and internal tools 🪞 Virtual Try-On & Visual AI — Let customers try clothing and products virtually before buying. Pose estimation, garment fitting, and photorealistic rendering built to drive e-commerce conversion ⚙️ LLM Integration — Wire Claude, GPT-4, or open-source models into your SaaS product, internal tool, or workflow with proper prompt engineering and memory management 🔁 AI Automation — Replace manual workflows with intelligent pipelines using LangChain, n8n, and Zapier. Trigger-based, reliable, and easy to maintain 📊 Full-Stack AI Apps — Dashboards, APIs, file processors, web scrapers, and MVPs built with FastAPI, Streamlit, Flask, Supabase, and Firebase 🚀 Startup MVPs — From rough idea to working AI product. Fast scoping, fast delivery, no bloat 🧰 Stack LLMs: OpenAI, Claude, Mistral, LLaMA Agents: LangChain, LangGraph, CrewAI RAG: Pinecone, Qdrant, ChromaDB, FAISS, embeddings Voice: ElevenLabs, Whisper, Deepgram Computer Vision: MediaPipe, OpenCV, YOLO, pose estimation Image Generation: Stable Diffusion, ControlNet, diffusion-based try-on Backend: Python, FastAPI, Flask, Supabase, Firebase Frontend & Dashboards: Streamlit, React basics Automation: n8n, Zapier, Make Infrastructure: Docker, Vercel, DigitalOcean, Azure, AWS basics Other: OCR, web scraping, REST APIs ✅ How I Work → I scope clearly upfront — no surprises mid-project → I ship working versions fast, then iterate → I communicate consistently — you always know where things stand → I think full-product, not just the AI layer