Hire the Best Machine Learning Engineers

Hello, I'm Tam 👋 - 7+ years of experience in Computer Vision, Deep Learning, and Generative AI. - 3+ years of experience in AI Automation, RAG, AI Agents. - Tech stack: Python, PyTorch, TensorFlow, OpenCV, FastAPI, Docker, CUDA, AWS, Modal, DeepStream, Javascript/TypeScript, NodeJS, NextJS, ReactJS, Electron, Tauri, PyQt - Built high-performance real-time object detection systems with NVIDIA DeepStream for edge and GPU deployment. - Developed OCR & document understanding pipelines for scanned documents, engineering drawings, and forms. - Built LLM/VLM-powered AI applications, including multimodal assistants, RAG systems, image analysis, and AI inference APIs. Let's turn your AI idea into a production-ready product.

Hello, I am investing all my time and resources in Upwork ☝ My experience covers data analysis, AI/ML model training, fine-tuning, and deployment to production on AWS, GCP, Azure, or edge devices. ⬣ Skills : GenAI : RAG, Vector databases, LLM finetune, AI Agent/Multi Agent systems. Machine Learning : classification, regression, similarity search. Computer vision : object detection&tracking, pose estimation, image processing. ⬣Programming languages : Python, MATLAB,C#. ⬣ ML/DL LIBRARIES : TensorFlow, Scikit-Learn, Keras, Pandas, Numpy, OpenCV,Pytorch, HuggingFace,Unsloth, Ultralytics. ⬣ Inference engines: llama.cpp, OLlama, LiteRT-LM, TensorRT. ⬣ Certificates : ✅ AWS Certified Solutions Architect Professional ✅DeepLearning.AI Machine Learning Engineer for production I AM READY TO IMPLEMENT YOUR PROJECT AND CONVERT YOUR IDEAS INTO A REALITY!

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 build production-ready RAG chatbots, voice AI agents, and autonomous AI systems — designed and deployed to hold up under real usage, not just demos. My work covers the full pipeline: retrieval architecture, agent design, voice integration, and cost optimization, so the LLM bill doesn't become a second project six months in. I don't just wire up an API to a model, I build systems that keep answering correctly and keep costing what they should, long after launch. My expertise includes: RAG Chatbots & Retrieval Architecture (Hybrid Search, Re-ranking, Semantic Caching) Autonomous AI Systems & Multi-Step Agent Workflows (LangGraph, Tool Calling, Memory) Voice AI Agents & Conversational IVR (Inbound Calls, Appointment Booking, Automated Follow-ups) LLM Cost Optimization (Query Compression, Token Budgeting, Sentence-Aware Chunking) Vector Databases & Semantic Search (Pinecone, pgvector, ChromaDB, FAISS) AI Workflow Automation & Custom Chatbots / Internal Knowledge Bases Full-Stack AI Applications (Python, FastAPI, n8n, Zapier) Cloud Deployment & Integration (AWS, Google Sheets/Calendar API, Docker) Recent projects include: Legal Document Intelligence Platform — Designed a RAG engine for plain-language Q&A over complex legal documents, plus a generation engine that drafts custody agreements, contracts, and divorce filings from structured input. Cut manual drafting time from hours to minutes. Shopify Live RAG Chatbot Pipeline — Built a crawler that continuously feeds live product data from a client's Shopify store into a RAG chatbot, giving customers accurate, real-time answers with zero manual updates. Voice AI Agent for a Salon (Vapi) — Built an inbound voice assistant that answers calls, books appointments directly into Google Sheets, and sends automated confirmation emails — full hands-off front desk automation. LeanRAG — Cost-Optimized RAG Architecture — Implemented four layered cost-optimization techniques (semantic caching, query compression, token budget enforcement, sentence-aware chunking) that cut LLM inference costs by 30–40% without losing answer quality. Karachi Air Quality Index Forecasting System — Built an end-to-end ML pipeline (data collection, preprocessing, model training, deployment) as a usable predictive tool for a non-technical team. What you can expect: Clean, maintainable, production-quality code with clear documentation Retrieval and voice systems tuned for accuracy, not just plausible-sounding answers Cost-aware architecture — token budgets and optimization built in from day one Clear communication and reliable post-deployment support I work with startups, growing businesses, and teams building AI products for measurable results, not experimental prototypes. If you're dealing with a support bottleneck, need a RAG chatbot that actually knows your documents, an autonomous system that runs a workflow end-to-end, or an LLM bill that's gotten out of hand, message me and I'll recommend the most practical approach.

I am an AI Engineer with 4+ years of experience building and deploying production-ready AI systems across classical machine learning, deep learning, computer vision, NLP, and Generative AI. Unlike many AI developers who focus only on LLMs, I work across the entire AI stack. I believe the best solution isn't always a large language model or an expensive API. Many real-world problems are better solved using classical machine learning or deep learning, resulting in lower infrastructure costs, faster inference, reduced latency, and greater control over your solution. My goal is always to build the most effective system not the most expensive one. Some of the areas I regularly work in include: * Classical Machine Learning (XGBoost, LightGBM, CatBoost, Random Forests, SVMs, feature engineering, predictive modelling, forecasting, anomaly detection, recommendation systems) * Deep Learning (PyTorch, TensorFlow, CNNs, Transformers, Vision Transformers, knowledge distillation, model optimization) * Computer Vision (object detection, image classification, segmentation, OCR, document understanding, face recognition, multi-object tracking, embedding-based search) * NLP & LLMs (RAG, GraphRAG, agentic workflows, fine-tuning, embeddings, semantic search, document QA, information extraction) * Generative AI applications using OpenAI, Anthropic, Gemini, and open-source models * End-to-end AI pipelines from data collection and preprocessing to training, evaluation, deployment, and monitoring I also have extensive experience optimizing AI models for production through knowledge distillation, pruning, quantization, and efficient inference, making models smaller, faster, and more cost-effective for both cloud and edge deployments. On the engineering side, I work comfortably with Python, FastAPI, PostgreSQL, pgvector, asynchronous programming, Docker, GPU acceleration, and cloud deployments. I build complete AI products and APIs that are designed to scale not just research prototypes. Beyond implementation, I enjoy solving difficult research and engineering problems. Whether it's designing a predictive model, improving model accuracy, reducing inference costs, building an intelligent document processing pipeline, or deploying an LLM application, I focus on solutions that are reliable, maintainable, and practical for production. I also lead a team of AI engineers, giving me experience not only in technical execution but also in planning, code quality, mentoring, and delivering projects on time. If you're looking for someone who can understand the problem first, choose the right AI approach, and build a production-ready solution that balances performance, cost, and scalability, I'd be happy to help.

AI Engineering Lead and PhD researcher in AI/ML, certified in Claude by Anthropic. I design and ship production systems built around Claude: agents, RAG pipelines, and automations that actually make it to deployment Over the past 7 years I've helped more than 20 companies put AI into production across the US, Europe, and the Middle East. I've led engineering teams at startups large and small, and I bring a consistent track record as a high performer on the work I take on I post regularly on Medium, X, and YouTube on the latest in AI, ML, and tech, which keeps me on top of how fast the field moves, with an audience of over 10,000 across platforms. I share this work publicly partly because teaching a thing is the best test of whether you understand it What I build: - RAG chatbots and agents over your documents, PDFs, Notion, and knowledge bases - LLM fine-tuning on your domain data - Workflow automations that replace 40–80% of manual operations - Solution architecture, so you commit to the right stack the first time - Recovery work on stalled or underperforming AI projects Send over the project and I'll reply the same day with a plan, a clarifying question, or an honest pass