I think like a scientist. I make people believe it. Molecular biologist (MSc). Years inside bioinformatics, clinical-trials technology, and corporate innovation at a global life-sciences company, and on my own time, a consumer brand built from zero to 650,000+ organic views, where I did everything: strategy, content, design, production. Clients don't hire me to just "write something up." They hire me to work out what their story actually means - to a customer, an investor, a room of executives - and then build whatever carries that meaning best. I work with companies whose product is hard to explain and whose credibility is the sale - supplements, biotech, medtech, AI, deep tech. That usually looks like: • Research & competitive analysis - the landscape, the evidence, the white space, in a report you can actually decide from • Brand positioning & messaging - what you say, to whom, and why you're different • Scientific claims review - what your studies actually support, and how to say it without losing the reader • Research-backed content - articles, scripts, email, web copy; every claim checked against the evidence • Investor, sales & executive decks - 50+ built for corporate leadership; complex science that lands in one read • Innovation & opportunity assessment - what's worth building next, and the business case that gets it funded Most companies split this between four people who never talk to each other: the researcher, the strategist, the writer, the designer. I'm the same person at every step, so nothing gets lost in the handoffs, because there aren't any. Send me your product page, your deck, or the claim you're least sure about, I'll reply with the first three things I'd change.

I am a former Research Scientist with more than 17 years of experience in biotechnology and life sciences. During my career at DSM, I worked in biochemical analytics, food technology, enzymes, and biofuels, supporting research, product development, and manufacturing through experimental design, laboratory research, and scientific data analysis. I hold an MSc in Life Science & Technology from Delft University of Technology with a specialization in biotechnology and biocatalysis. My background includes experimental design, data interpretation, scientific reporting, and solving complex technical challenges. Although I transitioned into a different career in 2019, I continue to apply the same analytical thinking, scientific methodology, and problem-solving skills that defined my research career. I am available for projects involving biotechnology, life sciences, scientific research, technical documentation, literature reviews, and research support. I value clear communication, reliability, and delivering high-quality work.

PhD scientist and strategist with a track record of translating complex technical problems into practical, data-driven solutions. My background spans R&D leadership, process optimization, and cross-functional project coordination. These are skills that transfer directly to technical consulting, strategy, and general business problem-solving. I'm especially passionate about sustainable food systems and alternative protein technology, having spent years working at the intersection of biology and food innovation. But my core strengths are broader: structured experimentation, rigorous analysis, clear technical communication, and getting teams aligned around a plan. I'm open to a wide range of engagements—from research and market analysis to process design, project management, and strategic consulting. I bring the same rigor to any problem I take on, whether it's scientific, operational, or business-focused.

Sarfaraz Ahmed – Applied AI Engineer | Agentic AI & Production GenAI I build production-grade AI systems — not demos, unstable chatbots, or prompt-only experiments. My focus is agentic AI, decision-support systems, and scalable GenAI pipelines that work reliably in real products. I help startups and teams turn GenAI ideas into stable, testable, and cost-controlled systems. I specialize in fixing the hard problems most teams face after the demo stage: • Hallucinations and inconsistent LLM behavior • Fragile RAG pipelines • Agents that fail on long-running tasks • High latency and uncontrolled API costs WHAT I BUILD Agentic AI Systems • Multi-agent systems with planning, execution, tool usage, and self-correction • Long-horizon task agents (not single-turn bots) • Supervisor, planner–executor, and recovery-based agent architectures • Reliable agent workflows using LangChain and LangGraph RAG & Knowledge Systems • Production-ready RAG pipelines over private data • Advanced chunking, metadata filtering, and hybrid search • Agentic RAG (agents that decide when and how to retrieve) • Vector databases and structured knowledge stores AI Automation & Decision Systems • Logic-driven AI tools with deterministic outputs where required • AI-powered internal tools for sales, operations, and research • Workflow automation using agents, APIs, and business rules Production Readiness • Tracing, evaluation, and observability (LangSmith-style workflows) • Cost optimization and latency reduction • Guardrails, failure handling, and safe tool execution • Multi-tenant and user-isolated AI systems RECENT WORK HIGHLIGHTS • Built a production multi-agent system (TryChirp) handling autonomous sales workflows using agent orchestration, memory, RAG, and API integrations • Designed AI prompt optimization and evaluation pipelines that reduced hallucinations and improved response reliability by over 90% • Delivered AI automation systems that replaced manual workflows and improved operational efficiency by 40–60% HOW I WORK • I separate decision logic from prompts • I design for determinism where business rules matter • I treat AI systems like software: testable, observable, and maintainable • I focus on outcomes, not buzzwords IDEAL PROJECTS • Agentic AI systems (LangChain / LangGraph) • RAG systems that must work reliably at scale • AI decision-support tools (finance, sales, operations, research) • Upgrading demo-level AI into production systems If you’re looking for someone who understands how GenAI actually fails in production — and how to fix it — I can help.

Experienced food scientist in quality assurance and research & development and I want to share my knowledge and expertise in food science

🚀 Hey amazing people! I’m Abrar, an AI Engineer focused on building production-ready AI systems using Agentic AI, RAG pipelines, LLM applications, and full-stack development. I build production-ready AI systems that combine intelligent reasoning (LLMs) with real-world, scalable applications (web, mobile, and backend) using modern stacks like LangChain, LangGraph, FastAPI, React, Flutter, and Node.js. My focus is not just building AI models, but delivering complete end-to-end AI products that are scalable, reliable, and production-ready. I help startups and companies build: ✔ AI Agents that automate complex workflows ✔ RAG systems powered by private or real-time data ✔ Intelligent chatbots with memory, tools, and reasoning ✔ Full-stack AI applications (web + mobile + backend) ✔ AI automation systems and data pipelines 🔥 CORE EXPERTISE ✔ Agentic AI & Multi-Agent Systems ✔ Retrieval-Augmented Generation (RAG) ✔ AI Chatbots & Conversational AI ✔ LLM Applications (GPT-4, Gemini, OpenRouter) ✔ Vector Databases & Semantic Search Systems ✔ AI Automation & Workflow Engineering ✔ Full Stack AI Application Development ✔ Computer Vision & Deep Learning Systems 🧠 AI / FULL STACK TECH STACK 🤖 AI & LLMs LangChain, LangGraph, CrewAI, OpenAI API, GPT-4.1, Gemini, HuggingFace, PyTorch, TensorFlow, Scikit-learn 🔍 RAG & VECTOR DATABASES Pinecone, FAISS, ChromaDB, MongoDB Vector Search, Weaviate, Qdrant, pgvector, Semantic Search, Hybrid Search, Embeddings ⚙ BACKEND & APIs FastAPI, Flask, Node.js, Express.js, REST APIs, Celery, Redis, Docker 📱 FULL STACK DEVELOPMENT React, Flutter, Firebase, Next.js, Dart, JavaScript, Python ☁ CLOUD & DEPLOYMENT AWS, GCP, Docker, CI/CD, Vercel, Railway 🧩 WHAT I BUILD I design and develop complete AI systems, including: • AI Agents with reasoning + tool execution • RAG chatbots using private knowledge bases • LLM-powered SaaS platforms • AI search engines using vector databases • Intelligent automation pipelines (ETL + AI workflows) • Full-stack AI applications (web + mobile + backend) • Computer vision analytics systems 🌟 WHY CLIENTS WORK WITH ME ✔ Production-grade engineering (not prototypes) ✔ Clean, scalable system architecture ✔ Strong expertise in AI + Full Stack development ✔ Fast communication and reliable delivery ✔ Focus on real business impact, not just models 🚀 Whether you're building a chatbot MVP, designing an advanced RAG-powered system with vector databases, fine-tuning a voice AI assistant, or deploying enterprise-grade Agentic AI systems, I help you turn ideas into fully production-ready solutions, from architecture to deployment. 𝐊𝐞𝐲𝐰𝐨𝐫𝐝𝐬: Python, Artificial Intelligence, Machine Learning, Data Science, Deep Learning, DeepSeek, AI Agent Development, AI Agentic Workflows, AI Workflow Automation, AI Builder, AI Co-pilots, RAG Chatbot, Custom ChatGPT Chatbots, AI SaaS Product Development, Web Development, AI Website Integration, Java, ScriptAPI, Natural Language Processing (NLP), NLP/NLU/NLG, Training Prediction Models, Text Classification, LSTM-CRF, GPT-3, GPT-4, GPT-4o, GPT Fine-Tuning, TensorFlow Serving, TorchServe, GANs, GCP, AWS, Azure AI, CI/CD, MLOps, LLMOps, REST API, REST API development (Flask, FastAPI), Prompt Engineering, Prompt Optimization, Software Development, AI Algorithms, Foundation Models, Large Language Model (LLM), Multimodal Large Language Model, Transformer Model, AI Applications, AI Chatbot Development, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, AI-enhanced Medical Imaging, AI-Generated Art, AI Voice Assistant, AI Automation Tools, Conversational AI, Image Analysis, Image Recognition, Natural Language Generation (NLG), Object Detection, Object Localization, Sentiment Analysis, Named Entity Recognition (NER), Vector Search, Pinecone Integration, ChromaDB, FAISS, RAG Architecture, Retrieval-Augmented Generation, LLM Pipeline, OpenAI Integration, Hugging Face Transformers, LangChain, LangGraph, LlamaIndex, Llama 2, Claude 3, Gemini, Falcon, Mistral 7B, Mixtral, Open Source LLMs, AI Development Language, Python Developer, Generative AI, GenAI Solutions, AIChatbot, LLM Solutions, ChatGPT Customization, AIEngineer, ConversationalAI, AIAgent, AIAgents, AI Prompt Engineer, FastAPI RAG Chatbot, AI SaaS Developer, AI Startup Development, VoiceBot Development, Serverless AI, OpenAI Whisper, Speech Recognition AI, Fine-tuning LLMs, Multi-language Chatbot, AI-Powered Recommendation System, AI for E-commerce, AI in Healthcare, AI in Education, Custom GPT Solutions, Custom AI Platform Development, Vector Databases, Embedding Models, Hybrid Search, OpenAI Consultant, AI Product Development, AI MVP Builder, No-code AI Integrations, AutoGPT, AgentGPT, GPT-4 Turbo, AI automation

Let's make the world healthier! I am a data/research scientist with 10+ years of deep involvement in neuroscience, biomedical engineering, and clinical research. My background includes digital signal processing, machine learning, statistical modeling & testing, and research design. Successfully-completed projects: 1. Automatic EEG artifact detection and editing 2. Time-series action recognition using acoustic and accelerometric data 3. Heartbeat-related ERP (HEP) visualization 4. ABX testing on food samples 5. Designing wearable sensors 6. Human performance analysis using PPG and GSR data 7. Heart-rate variability (HRV)-based clinical diagnosis 8. Implementing an online EEG/SSVEP-based brain-computer interface (BCI) 9. Automatic sleep-stage classification using deep neural network 10. Unsupervised clustering (Kmeans, UMAP) clinical data for individualized medicine 11. Academic research (50+ peer-reviewed articles, 3000+ citations, 100+ peer-reviews) Skillset: - Programming: Python, MATLAB, R, BigQuery - ML libraries: Scipy, Scikit-Learn, Pytorch, Pandas, Numpy - Data visualization: Matplotlib, ggplot2, Tableau - Electrophysiology: EEG (research-grade/wearable), ECG, EMG, EOG, PPG, GSR, accelerometer - Data acquisition: LabStramingLayer (LSL) - EEG analysis: EEGLAB, MNE - Version control: Github - Editing: LaTeX, BibTex

Machine Learning Expert with 6+ years of experience and 100+ completed projects, specializing in OCR, Computer Vision, LLMs/RAG, and MLOps — from training and fine-tuning to inference and production deployment. I containerize services with Docker/Docker Compose and deploy on AWS and GCP, optimizing for cost and low latency. Skills: Python, PyTorch, TensorFlow, Hugging Face/Transformers, OpenCV, YOLO, ViT, VGG, ResNet, DocTR, Tesseract, AWS Textract, Google OCR, OpenAI API, LLAMA-3, ChromaDB, Pinecone, Docker, AWS, GCP Types of projects I have completed in recent years - #1 OCR / Document Extraction - NLP expert to extract information from natural text — Extract information from images and PDFs using OCR - Computer Vision Expert for Web App (OCR + AutoML) — Testing kit detection from images and reading test results - NLP expert to extract information from natural text — Finding names, company names, etc. from images and PDFs - Computer Vision Engineer for Post-Tension Structural Drawing Analysis (PDFs) — 2D drawing analysis #2 Computer Vision - Object Detection/Counting Project — Iron rod counting in batches using YOLOv8 - Object detection for ice hockey video — Detecting players, puck, and possession based on activity - Mechanical Component Segmentation and Clustering — 2D drawing analysis - AI/ML Engineer, Python/React for Skincare App — ML model integration in React Native app #3 LLM / Generative AI - Prototype Real-Time Text-to-Speech AI — Calling agent using OpenAI (GPT-4, Whisper), Google TTS, and Twilio - Llama-2, Long Llama Engineer — Using open-source LLM models - Data Engineer ML/AI — Customer care chatbot using OpenAI and LLaMA-7B - AI Training for Foreign Language — Fine-tuning LLM models #4 Regression / Predictive Modeling - Cloud-based Data Scientist (AWS) — Models for fintech (NLP, Tabular) - Stock market price prediction — Using historical data to predict future prices - Balance/debt forecasting — Using bank statement data to predict debt and balance #5 Recommendation Systems - Food Recommendation Engine — Correlating users and food menu item based on description - Similar Products based on Users Interest — Find the product a user might be interested in - Perfume suggestion based on User Profile — Suggest existing perfumes based on users profile

I help biotech companies, pharma teams, and research institutions turn complex genomic and multi-omics data into clear, publication-ready insights — on time and with full reproducibility. What I specialize in: - Bulk RNA-seq & single-cell RNA-seq (scRNA-seq) analysis - Spatial transcriptomics (Visium) - DNA methylation, metabolomics & microbiome data - Differential gene expression & splicing analysis - Pathway & gene set enrichment analysis (GSEA) - Network analysis & pseudotime inference - Dimensionality reduction & unsupervised clustering - Multi-omics data integration (single-cell, spatial, immune repertoire, clinical metadata) Tools & Stack: R/Bioconductor and Python — with a strong focus on clean, well-documented, reproducible pipelines and publication-quality figures. How I work: I adapt to data quality issues, complex experimental designs, and tight timelines. Every deliverable includes clear documentation so your team can build on the results. I hold a 100% Job Success Score and Top Rated status on Upwork. Send me a message describing your dataset and your goal — I will tell you honestly whether and how I can help.

Data Scientist applies their professional knowledge of computer science, mathematical, statistical theories, techniques, and methods to gather, analyze, design and assist in the construction of new processes for analytical modeling, model interpretation, and/or assisting in the reporting of quantitative information, data trends, data relationships and data correlations among or within data.