Hire the Best Natural Language Understanding Specialists

I build AI applications, data pipelines, analytics solutions, computer vision systems, and automation tools using Python. My work spans Generative AI, Retrieval-Augmented Generation (RAG), data engineering, big data, statistical analysis, machine learning, computer vision, dashboards, and backend development. I can help take a project from raw data, documents, images, or business workflows to a working system, automated pipeline, dashboard, API, or deployed AI solution. What I can help you with: Generative AI and LLM applications Retrieval-Augmented Generation (RAG) systems AI agents, chatbots, and knowledge assistants Python automation and API integrations FastAPI backend development Data engineering and ETL pipelines PySpark, Apache Spark, and Databricks Big data processing and performance optimization SQL data modeling and database workflows Data cleaning and exploratory data analysis Statistical analysis and KPI reporting Tableau and Power BI dashboards Machine learning and predictive modeling Computer vision and image processing YOLO object detection and OCR OpenCV-based automation Document processing and intelligent search Recent projects include an AI telecom engineering copilot that analyzes KPI datasets and technical documentation using RAG, a PySpark and Databricks platform processing 23M+ financial records, an LLM-powered WhatsApp business automation assistant, a machine-learning cellular network analytics system, and computer vision pipelines for OCR and image analysis. Technical stack: Python, SQL, Pandas, PySpark, Databricks, Apache Spark, Scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, RAG, LLM APIs, FastAPI, OpenCV, YOLO, Docker, Git, GitHub Actions, Supabase, PostgreSQL, REST APIs, Tableau, Power BI, and Linux. My engineering background also includes telecommunications, IoT, networking, and statistical signal/data analysis, which helps me work effectively on technical and domain-specific projects rather than only generic software applications. I’m available for projects involving AI systems, data engineering, analytics, computer vision, automation, and Python backend development.

I build AI systems that make it to production: machine learning and NLP pipelines, LLM and RAG applications, AI agents, and voice AI used by real customers every day. 𝐑𝐞𝐜𝐞𝐧𝐭 𝐖𝐨𝐫𝐤 - Voice AI agent platform at a healthcare technology company - Clinical document extraction system on vLLM hitting 99.99%+ accuracy on messy, unstructured files - Internal agentic coding assistant that cut time spent on repetitive engineering workflows by 80% - LiveQ, an AI desktop assistant I founded (Electron and Next.js on LiveKit): designed the full agent architecture and ran 50+ customer interviews in the first month 𝐌𝐋 𝐚𝐧𝐝 𝐍𝐋𝐏 𝐁𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝 My experience goes deeper than the LLM wave. At Penn State's NLP lab I built a conversational agent deployed to Alexa devices reaching a 50M+ user base, and worked hands-on with transformer models (BERT, T5, XLNet), NER pipelines, OCR, and topic modeling. That history means I know when your problem needs a fine-tuned classifier instead of a frontier model, and when it doesn't need ML at all. 𝐖𝐡𝐚𝐭 𝐈 𝐂𝐚𝐧 𝐇𝐞𝐥𝐩 𝐖𝐢𝐭𝐡 - LLM applications and RAG pipelines with strict accuracy targets - AI agents and tool integrations (function calling, MCP) - Voice AI and real-time interaction systems - NLP and document AI: extraction, classification, intelligent OCR - End-to-end automation, from backend services to a polished UI 𝐒𝐭𝐚𝐜𝐤 𝐚𝐧𝐝 𝐂𝐫𝐞𝐝𝐞𝐧𝐭𝐢𝐚𝐥𝐬 Python, TypeScript, LangChain, LiveKit, Ray Serve, vLLM, Next.js, AWS and GCP. MS in Computer Science (AI) from USC. 𝐇𝐨𝐰 𝐈 𝐖𝐨𝐫𝐤 I move fast, take ownership, and communicate clearly. Send me a message with what you're building, and I'll tell you honestly whether I'm the right fit and how I'd approach it. Keywords: Artificial Intelligence, Machine Learning, Deep Learning, NLP, Natural Language Processing, Generative AI, LLM, Large Language Models, ChatGPT, OpenAI, Claude, Llama, RAG, Retrieval Augmented Generation, Vector Database, Embeddings, AI Agent, Agentic AI, Multi-Agent Systems, MCP, Model Context Protocol, Function Calling, Voice AI, Conversational AI, Chatbot, Speech-to-Text, Text-to-Speech, LiveKit, vLLM, LangChain, Ray Serve, Prompt Engineering, LLM Deployment, Document AI, Intelligent Document Processing, Data Extraction, OCR, Named Entity Recognition, BERT, Transformers, AI Automation, Workflow Automation, Python, TypeScript, Next.js, React, Electron, AWS, GCP, Healthcare AI, Full-Stack Development, Real-Time Systems

3+ years of Experience as Full-Stack AI Engineer⚡|| LLMops Specialist || 🎯Delivered production-grade AI solutions for enterprise clients including AMD and Emerson || Product Development & Monitoring || Architect Solutions of ML DL NLP LLM Hello👋 I'm Mahendra, and I'm truly excited to have you here on my profile today ! ✨ ⚡ With over a 3+ years of experience and a specialized degree 🎓 in Data Science establishes a solid foundation of trust 🔒 and assurance 🤝 - 💼 A highly dedicated and results-oriented Data Scientist with a strong background in AI and passion for solving complex business problems. -🎯Skilled in Machine Learning, Deep Learning, NLP, Generative AI and AIOps -🚀Adept at building end to end pipelines, developing ML, DL, NLP, LLM Applications, deploying models seamlessly to production ☁️ 🛠️ Technical Skills: ✅ AI Frameworks: LangChain | LlamaIndex | Agno | LangGraph | AutoGen | Hugging Face | Crew AI ✅ AI Models: OpenAI | Gemini | Llama | Hugging Face | Claude | Ollama | Mistral | DeepSeek | GLM | Kimi ✅ AI Tools Harnesses : Claude Code | Google Antigravity | OpenCode | OpenAI Codex ✅ Data Storage & Vector Database: SQL | MongoDB | Pinecone | ChromaDB | Faiss | Postgres | Azure AI Search | Milvus | Qdrant | Weaviate | pgvector ✅ Methodologies: Machine Learning | Deep Learning | NLP | Generative AI (LLM) ✅ Programming Languages & Libraries: Python | pandas | NumPy | Matplotlib | seaborn ✅ Tools: MLFlow | DVC | Docker | Git & GitHub | Flask | Airflow | Evidently AI | Fast API | Pydantic ✅ Frameworks: Scikit-learn | TensorFlow | Keras | PyTorch | LangChain ✅ Data Science Techniques: Data Pre-processing | Feature Engineering | Feature Selection Model Building Evaluation | Model Monitoring ✅ Cloud Services: AWS | Azure | Docker Hub ✅ AI/ML Operations: Deployment | Monitoring | Experimentation ✅ CI/CD: GitHub Actions ✅ OS: Linux ✅ Dashboarding: Tableau | Microsoft Excel | Statistical Analysis Generative AI Techniques ✨ Advanced RAG & Agentic RAG - Hybrid Search + Reranker ✨ Chunking Strategies ✨ LLM Summarizer ✨ AI Memory ✨ Chatbots-Q/A ✨ Knowledge Graphs ✨ Fine Tuning LLM ✨ AI Agents , SQL Agents & Multi Agents ✨ Prompt Engineering ✨ Open source & Closed Source AI Models ✨ MCP Servers ✨ PII Detection ✨ LLM Evaluations - Retriever, Generator Evaluators ✨ Custom AI Enterprise Solution DATASCIENCE TECHNIQUES ✔️ Collecting Raw Data ✔️ Defining Problem statement ✔️ Exploratory Data Analysis ✔️ Feature Engineering ✔️ Feature selection ✔️ Model Building ✔️ Hyperparameter Tuning ✔️ Model Evaluation Machine Learning Techniques ⚡ Classfication ⚡ Regression ⚡ Clustering ⚡ Outlier Detection ⚡ Tree & Non Tree ML Models ⚡ Hyperparameter Tuning ⚡ Ensemble Learning - Bagging, Boosting, Stacking ⚡ Evaluation Metrics Performance Optimization NLP & Deep Learning Techniques 💫 Text Classification 💫 Sentimental Analysis 💫 NLP text Preprocessing 💫 Text Encoding Techniques 💫 Word Embeddings 💫 ANN, LSTM, RNN 💫 Transformer Architecture (LLM) 💥 END-TO-END DATA SCIENCE PROJECT EXPERTISE 💥 ADEPT AT BUILDING END TO END PIPELINES ETL 💥 PREDICTIVE MODEL, GEN AI PRODUCT DEVELOPMENT 💥 PRODUCT DEPLOY 💥ARCHITECT SOLUTIONS OF ML DL NLP GENAI 🏆 Achievements: 👉 Demonstrates my ability to design innovative ML, DL, NLP, GENAI solutions tailored to your needs. 🏅 Top 6% in a global Kaggle Data Science competition with a custom machine learning stacking Model No AutoML tools used. 🚀 Over 50+ end-to-end projects on GitHub showcasing expertise in Machine Learning, Deep Learning, NLP, and Generative AI. 📂End-to-End Projects I Have Developed: 🔹 RAG | Multi-Agent RAG | Fine-Tuning LLM | SQL Agents | LLM Summarizer 🔹 Llama Index Azure AI Search Advanced accurate Production level RAG 🔹 LangGraph Multi AI Agent Routing between (RAG + SQL + Normal Q&A) 🔹 Accurate Advanced Agentic RAG 🔹 Cost effective Earnings Call Transcript LLM Summarize 🔹 Medical Chatbot | LLM + Custom PDF Data 🔹 Steel Plant Load Prediction 🔹 Retail Price Optimization 🔹 Sentiment Analysis App 🔹 Medical Insurance Price Prediction 🔹 Customer Attrition Prediction 🔹 MCQ Generator | LangChain + Huggingface LLM 🔹 Insurance Cross-Sell Prediction 🔹 MongoDB Connect | Streamlining Database Connectivity 🔹 Restaurant Revenue Prediction 🔹 Sales Prediction App 💡 Let’s Collaborate: 📌 I'm eager to bring my expertise in data science to your projects and deliver exceptional results. 📌 Let’s collaborate to quickly bring your projects from concept to reality and turn your data challenges into success stories! Would love to hear back from you soon! 😊

Greetings! I'm Salah Sammari, a dedicated Data Scientist with a focus on Natural Language Processing. Having accumulated over two years of hands-on experience in the realm of AI and machine learning, I'm reaching out to offer my expertise for your AI-driven endeavors. Professional Snapshot: My journey began with a solid foundation in Computer Science Engineering from the Higher School of Engineers Esprims in Tunisia. Over the past two years, I've been privileged to work with distinguished organizations such as DNEXT Intelligence SA and UBIAI. In these roles, I've not only implemented advanced NLP solutions but also successfully navigated challenges in trading platform optimization and extended data science training to budding enthusiasts. Core Competencies: NLP & Machine Learning: Expertise in various techniques ranging from sentiment analysis, topic modeling to Named Entity Recognition (NER). I've extensively worked with transformer models such as GPT, BERT, and LayoutLM. Programming & Tools: Proficient in Python and SQL (Postgres) with a keen understanding of data science libraries like Pandas-Numpy, Matplotlib-Seaborn, and Scikit-learn. My skill set also includes cloud platforms like AWS and Snowflake. Project Highlights: From developing AI-driven solutions for content filtering and recommendation engines to building transformer-based chatbots and leveraging OCR techniques, I've overseen multiple projects that required innovative problem-solving and rigorous model fine-tuning. Collaboration & Training: My cross-functional collaboration experience ensures smooth project executions. Additionally, as a Data Science Trainer at Ruspina Training Center, I've mentored over 150 students in Python, machine learning, and NLP. What Drives Me: I thrive on challenges and continually seek opportunities to apply my skills in diverse scenarios. My rank as a Kaggle Master, standing in the top 1%, speaks volumes about my passion for pushing the boundaries of what AI can achieve. The blend of rigorous academia, practical applications, and my incessant drive to learn has shaped my holistic approach to problem-solving.

I am not just an AI Engineer; I am a storyteller who connects the dots between complex data and business growth. With 5 years of hands-on experience and a robust academic foundation in Statistics and Engineering, I specialize in building AI systems that don't just work they innovate. Why work with me? I don’t just deliver code; I translate your high-level business needs into high-performing, production-ready AI systems that solve real-world bottlenecks. My Core Expertise: - AI Solutions: Text analysis & image recognition - AI Search: Smarter answers with RAG & advanced prompt design - Custom AI Models: Tailored GPT, Gemini, LLaMA, Claude & more - Vibe Coding: Cursor, Lovable, Antigravity, etc.. - AI Workflows: Multi-agent automation for complex tasks - Voice AI: Text-to-speech & speech-to-text (AWS, Google, Azure) - AI Visuals: From idea to image using DALL·E, Midjourney, Stable Diffusion - Automation: Zapier, Make, n8n & custom workflows - Smart Pipelines: Event-driven triggers, error handling & smooth operations AI Agents & Chatbots: I build sophisticated multi-agent and RAG frameworks. Examples include E-commerce virtual associates that drive sales and POS customer support agents that handle complex queries autonomously. Text-to-SQL & Analytics: I enable non-technical users to "talk to their data," providing instant, natural-language insights into sales, inventory, and KPIs. Intelligent Automation (n8n): I streamline operations by eliminating repetitive tasks. My AI-powered HR Agent workflow automatically parses, scores, and ranks candidates to find your "best fit" instantly. Computer Vision & OCR: Expert in YOLO and Qwen2.5-VL. I automate data entry from handwritten or digital invoices directly into structured JSON for accounting and inventory software. Full-Stack AI Deployment: I take models from notebooks to production. Expert in the full AI lifecycle, including MLOps, containerization (Docker), and scalable cloud deployment on GCP. The Toolbox: Frameworks: PyTorch, Keras, TensorFlow, Scikit-learn, OpenCV. LLM Ops & Orchestration: LangChain, LangFlow, DSPy, OpenAI API, Apple MLX. Deployment: Docker, GCP, MLOps pipelines. I am dedicated to delivering results that exceed expectations always on time and within budget. Let’s build your success story. Click the 'Invite' button to start a conversation!

I’m an AI Agent & Integration Engineer with 3+ years of experience building production AI systems that connect LLMs to real business data, tools, APIs, and workflows so AI can reliably operate inside real products and businesses. I specialize in AI Agents, RAG, MCP servers, LLM applications, AI automation, and Python backends, helping startups and technical teams turn AI prototypes into reliable systems that can safely work with databases, internal knowledge, APIs, documents, and business workflows. What I build ★ AI Agents & AI Automation Multi-agent systems, tool-calling agents, LangGraph workflows, state machines, memory, routing, retries, guardrails, and human-in-the-loop workflows. ★ RAG & Enterprise Knowledge Systems Production RAG pipelines, document ingestion, hybrid/vector search, Graph-RAG, retrieval optimization, citations, access control, and hallucination guardrails. ★ MCP Servers & AI Tool Integration Secure Model Context Protocol (MCP) servers that connect AI agents to PostgreSQL, APIs, internal systems, and business tools with controlled permissions and safe execution. ★ LLM & Generative AI Applications OpenAI, Anthropic, Gemini, and AWS Bedrock integrations; structured outputs, prompt engineering, caching, fallback systems, evaluation, and cost optimization. ★ Text-to-SQL & AI Data Assistants Natural-language analytics over PostgreSQL and other databases with SQL validation, schema-aware RAG, read-only execution, query auditing, and security controls. ★ Document AI, OCR & Computer Vision Large-scale OCR preprocessing, document extraction, YOLO-based detection/tracking, image processing, and AI-powered video analysis. Recent production work ★ Built a secure Text-to-SQL MCP server for PostgreSQL with AST-based SQL validation, read-only database transactions, statement timeouts, query cost controls, schema-aware RAG, REST API support, 158 tests, and 100% test coverage. ★ Built a Text-to-SQL analytics assistant over a 3M-row PostgreSQL sales database using AWS Bedrock, with SQL validation, intent matching, automatic retry, query caching, fiscal-calendar date resolution, session memory, and an audit interface. ★ Built Rabt, a Graph-RAG engine for codebases, using AST analysis and runtime instrumentation to identify the minimal context required by an LLM, reducing context by 99.2% compared with full-repository baselines. ★ Built a 20M+ document OCR preprocessing pipeline using Python/OpenCV with parallel processing and resume-safe batch execution for large-scale legal/property records. ★ Built a YOLOv8 + EfficientNet computer-vision pipeline for sports video analysis, achieving 0.954 mAP50 and 96.3% validation accuracy and reducing manual footage-review time by 70–90%. ★ Built production RAG applications with FastAPI, LangChain, Pinecone, OpenAI APIs, RBAC, hybrid retrieval, and citation-based hallucination guardrails. My engineering approach I care about what happens after the demo. That means: * Secure tool access and permissions * Deterministic validation around LLM outputs * Reliable fallback and retry mechanisms * Efficient retrieval and context management * API and database security * Observability and testing * Token and infrastructure cost control * Clean, modular Python * Dockerized and production-ready deployments I don't just connect an LLM API and call it an AI system. I design the surrounding infrastructure that makes the system reliable enough to use with real users and real business data. Core stack Python, FastAPI, AI Agents, LangGraph, LangChain, RAG, Graph-RAG, MCP, LLMs, Generative AI, OpenAI, Anthropic, Gemini, AWS Bedrock, PostgreSQL, SQL, Vector Databases, Docker, REST APIs, PyTorch, YOLO, OpenCV, OCR, Computer Vision, Next.js, React, TypeScript. If you're building an AI product, internal AI assistant, agentic workflow, RAG system, MCP integration, Text-to-SQL application, or AI automation pipeline, I can help take it from prototype to production.