Hey there! I'm Taha Yassine, your go-to expert in Computer Vision, Machine Learning, and Deep Learning engineering. With a passion for cutting-edge technology and a knack for solving complex problems, I have worked on 10+ complex computer vision projects, bringing a lot of experience to the table. In the realm of Computer Vision, I specialize in developing robust algorithms for object detection, image classification, facial recognition, and semantic segmentation. Whether it's detecting anomalies in medical images or enhancing surveillance systems for security applications, I thrive on pushing the boundaries of what's possible. My expertise extends to Machine Learning and Deep Learning, where I have a proven track record of building and deploying scalable models for various domains, including healthcare, finance, and retail. From predictive analytics to natural language processing, I leverage state-of-the-art techniques to extract meaningful insights and drive actionable results. What sets me apart is my commitment to delivering high-quality solutions tailored to your specific needs. I take pride in collaborating closely with clients to understand their requirements, ensuring that the end product exceeds expectations. Let's collaborate to bring your vision to life. Whether you need assistance with a one-time project or ongoing support, I'm here to help you harness the power of AI and advance your business objectives. Get in touch, and let's make magic happen!

I have spent 8 years at the intersection of data, AI, and the question nobody wants to ask “does it actually deliver results?” From forecasting systems to LLM pipelines and autonomous multi-agent systems built for real world problems where off-the-shelf solutions fail. The tools change with every project. The bar doesn't. Here is an overview of my Stack 𝗠𝗟 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, CatBoost, statsmodels 𝗟𝗟𝗠𝘀 & 𝗡𝗟𝗣: Open AI, Claude, Gemini, Grok, LLaMA, Mistral, DeepSeek, BERT, BART, SetFit, HuggingFace 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: LangChain, LangGraph, RAG Pipelines, n8n, Make, OpenAI API, Anthropic API, Lovable, OpenClaw 𝗩𝗲𝗰𝘁𝗼𝗿 & 𝗦𝗲𝗮𝗿𝗰𝗵: Pinecone, FAISS, ChromaDB, SentenceTransformers, Embeddings 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: pandas, NumPy, Parquet, Airflow, dbt, ETL Pipelines 𝗔𝗣𝗜𝘀 & 𝗦𝗰𝗿𝗮𝗽𝗶𝗻𝗴: FastAPI, Flask, WebSocket, PRAW, BeautifulSoup, Selenium 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Matplotlib, Seaborn, Plotly, Tableau, PowerBI, SHAP 𝗖𝗹𝗼𝘂𝗱 & 𝗜𝗻𝗳𝗿𝗮: AWS EC2, SageMaker, AWS Bedrock, Firebase, Docker, VPS 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 & 𝗔𝗽𝗽𝘀: React, Next.js, Streamlit, Gradio, Lovable 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Gmail API, Google Calendar API, WhatsApp API, Stripe, PayPal, Odoo You can get a feel for the work pretty quickly. Here's a slice. → 𝗔𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • Built a 𝒇𝒖𝒍𝒍-𝒄𝒚𝒄𝒍𝒆 𝑨𝑰 𝒉𝒊𝒓𝒊𝒏𝒈 𝒑𝒊𝒑𝒆𝒍𝒊𝒏𝒆 using n8n to orchestrate OpenAI-powered resume parsing with Gmail, Google Sheets, and Calendar APIs reducing 𝐻𝑅 𝑚𝑎𝑛𝑢𝑎𝑙 𝑤𝑜𝑟𝑘𝑙𝑜𝑎𝑑 𝑏𝑦 80% with centralized candidate tracking and automated scheduling. • Developed 𝒂 𝒓𝒆𝒂𝒍-𝒕𝒊𝒎𝒆 𝑨𝑰 𝒗𝒐𝒊𝒄𝒆 𝒂𝒈𝒆𝒏𝒕 supporting voice-to-voice, speech-to-text and text-to-text conversations via FastAPI and WebSocket with ultra low latency using GPT for dialogue management. • Built an 𝑨𝑰 𝒑𝒐𝒘𝒆𝒓𝒆𝒅 𝒕𝒆𝒍𝒆𝒎𝒆𝒅𝒊𝒄𝒊𝒏𝒆 𝒑𝒍𝒂𝒕𝒇𝒐𝒓𝒎 on Next.js and Firebase with role-based AI prompts, automated symptom collection and 𝑟𝑒𝑎𝑙 𝑡𝑖𝑚𝑒 𝑐𝑙𝑖𝑛𝑖𝑐𝑎𝑙 𝑖𝑛𝑠𝑖𝑔ℎ𝑡𝑠 for patient doctor interaction. → 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 From pharmaceutical supply chains to crypto markets, I build forecasting systems that drive real inventory, budget and trading decisions. • Built a 3𝑴+ 𝒓𝒆𝒄𝒐𝒓𝒅 𝒑𝒉𝒂𝒓𝒎𝒂 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕𝒊𝒏𝒈 𝒔𝒚𝒔𝒕𝒆𝒎 pipeline: XGBoost R²=0.90, 20% accuracy gain, 17-chart EDA uncovering SKU concentration risk and billing-cycle demand patterns • 𝑪𝒓𝒄𝒓𝒚𝒑𝒕𝒐 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕𝒊𝒏𝒈 𝒎𝒐𝒅𝒆𝒍𝒔 using ARIMA + Reddit sentiment (PRAW + SetFit) → BUY/SELL/HOLD signals for BTC, ETH, SOL, DOGE • 𝑫𝒆𝒎𝒂𝒏𝒅 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕𝒊𝒏𝒈 𝒑𝒊𝒑𝒆𝒍𝒊𝒏𝒆 (LR, XGBoost, RF, LSTM) achieving R²~0.99 used car price prediction deployed via Flask → 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 I build classification, regression, and validation systems with rigorous evaluation not just accuracy scores but defensible, 𝒑𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏-𝒓𝒆𝒂𝒅𝒚 𝒎𝒐𝒅𝒆𝒍𝒔. • SVM, Gradient Boosting, MLP, XGBoost, Logistic Regression always with GridSearch and KFold CV for hyperparameter integrity • Diabetes detection: 86% accuracy on 3-class imbalanced clinical dataset with feature engineering and undersampling experiments → 𝗡𝗟𝗣 & 𝗟𝗟𝗠-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 I combine classical text modeling with modern LLMs to extract structured insight from unstructured data at scale. • Claude 3.5 Sonnet (AWS Bedrock) + BART MNLI + SentenceTransformer pipeline quantifying open ended survey sentiment for fragrance product strategy • Real-time Reddit 𝒔𝒆𝒏𝒕𝒊𝒎𝒆𝒏𝒕 𝒅𝒂𝒔𝒉𝒃𝒐𝒂𝒓𝒅 for ASTS ticker upvote-weighted transformer scoring with daily trend visualization • 𝑻𝒆𝒙𝒕 𝑪𝒍𝒂𝒔𝒔𝒊𝒇𝒊𝒆𝒓 across disaster tweets (TFIDF, 80%), IMDB reviews (LSTM, 86%) and news categorization (CNN + GloVe, 75%) • GPT-4o, Claude, LLaMA, Grok and Mistral used as deliberate data enrichment and annotation tools inside ML pipelines I work with startups building their first AI product, enterprises with complex data problems, and individuals with unique challenges nobody else wants to touch. If the problem is hard and the data is messy that's exactly where I do my best work. Send me a message and let's figure out if I'm the right fit. I will tell you within 24 hours whether I can help and how.

Most AI initiatives are not unsuccessful because of substandard models. They are not working after all no one assembles all the pieces together. 𝐓𝐡𝐚𝐭'𝐬 𝐦𝐲 𝐣𝐨𝐛. ᯓ★ I build production-ready AI systems that solve real business problems. 🌍 Serving clients globally with 𝐟𝐥𝐞𝐱𝐢𝐛𝐥𝐞 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 across all time zones 🏆 3+ years of hands-on experience across full-stack 𝐀𝐈 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 😊 Trusted by 100+ 𝐜𝐥𝐢𝐞𝐧𝐭𝐬 across healthcare, legal, finance, and education 🔝 Specialized in 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 from concept to deployment Most AI projects do not fail because the model is weak. They fail because the full system is not designed properly: retrieval is poor, APIs are fragile, the frontend is disconnected, or deployment is ignored. My work focuses on connecting all of those pieces into one reliable product. I work across the full stack of AI development: machine learning/deep learning models, computer vision/NLP techniques, GenAI/Agentic AI & LLM applications, RAG pipelines, backend APIs, frontend interfaces, databases, and cloud deployment. I have built AI solutions across healthcare, legal tech, education, analytics, and cybersecurity, including systems for clinical triage, legal document analysis, real-time sentiment analysis, and enterprise knowledge assistants. ➥ Core Expertise Machine Learning: Classification, regression, clustering, dimensionality reduction, feature engineering, ensemble learning, anomaly detection, time-series, model evaluation (ROC-AUC, F1, RMSE), pipeline optimization. Deep Learning: CNNs, RNNs, LSTMs, Transformers, attention mechanisms, transfer learning, fine-tuning, backpropagation, gradient descent, model quantization. Computer Vision: Image classification, object detection, segmentation, OCR, pose estimation, preprocessing, augmentation, visual embeddings, video analysis, Grad-CAM. NLP: Text classification, sentiment analysis, NER, topic modeling, summarization, question answering, machine translation, semantic search, embedding-based retrieval. GenAI / Agentic AI: RAG pipelines, multi-agent systems, tool/function calling, prompt engineering, embeddings & vector search, reranking, hallucination reduction, LLM evaluation, fine-tuning (LoRA/QLoRA). Deployment: Containerization, orchestration, CI/CD pipelines, autoscaling, monitoring & logging, GPU deployment, serverless architecture. Full-Stack AI Development: AI chatbots, copilots, document Q&A systems, RAG-based systems, multi-agent workflows, memory systems, local LLM deployment. ➥ Tools and Frameworks Machine Learning: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, NumPy, pandas, MLflow, Optuna. Deep Learning: PyTorch, TensorFlow, Keras, Hugging Face Transformers Computer Vision: OpenCV, PyTorch Vision, Detectron2, YOLO, Albumentations, MediaPipe, Segment Anything (SAM), FiftyOne. NLP: BERT, RoBERTa, T5, GPT, SentenceTransformers, spaCy, NLTK, Hugging Face Transformers. GenAI / Agentic AI: OpenAI API, Gemini, LLaMA, Mistral, Claude, LangChain, LangGraph, CrewAI, LlamaIndex, FAISS, ChromaDB, Pinecone, Milvus, Ollama, vLLM. Backend APIs: FastAPI, Flask, Django, Node.js, GraphQL, REST APIs, Redis, Nginx, PostgreSQL/Supabase, Firebase. Deployment: Docker, Kubernetes, AWS, GCP, Azure, Vercel, GitHub Actions. ➥ Why Clients Work With Me - I build complete AI products, not just isolated models - I focus on real-world usability, performance, and maintainability - I communicate clearly and give realistic technical direction - I write clean, documented code that teams can extend - I can take a project from idea to deployment If you need an AI engineer who can handle the full pipeline from LLMs and ML models to backend, frontend, and deployment, I would be glad to help. Let’s have a quick chat/meeting and discuss the solution to your problem. ➥ EXPERTISE Machine Learning | Deep Learning | Generative AI | Agentic AI | NLP | AI System | AI Development | AI Full-stack Development | AI MVPs | Python Scripting | Computer Vision | Sentiment Analysis | RAG Chatbots | Automations | Backend Development | Frontend Development | AI Chatbots | LLM Applications | AI Web App Development | AI Engineer | Cloud Deployment | Classifications | Recommendation systems | EDA | Feature Engineering | Time-series | Predictive Modelling | MLOps | PDF Extraction | Data Extraction | Object Detection | Model Fine-tunning | AI Model Integration | AI PDF Extraction | Statistical analysis | Legal | Finance | Healthcare | Education | AI Model Development | Full-stack Development

Hello 👋 I’m Marlon — a senior-level AI developer, machine learning architect, and founder of Starbourne Labs. I specialize in building and scaling real-world, production-grade AI systems — from advanced LLM-powered agents to GPU-optimized infrastructure and real-time ML pipelines. 🚀 RESULTS • Delivered 20+ AI/ML products, helping founders raise over $30 million in VC funding • Scaled Merciv.ai to $3M+ ARR using multi-tenant LLM microservices with sub-300ms latency • Built a GPT-4o support agent at Form Labs handling over 2 million messages per month • Deployed LLM chatbots serving 50,000+ users across 12 industries • Reduced inference costs by 70% using quantization (QLoRA, INT8, AWQ) and serverless GPU orchestration • Boosted agent accuracy by 40% with better tool-use, memory routing, and embedding optimization 🤖 LLM Chatbots & Agentic Systems • GPT-4o, Claude 3 Opus, Gemini 1.5, Llama 3, Mistral • LangGraph, LangChain, AutoGen, CrewAI, OpenAI Tools • Tool use, function calling, memory, persona control, ReAct workflows • RAG pipelines with pgvector, Qdrant, Pinecone, Weaviate • API-integrated agents with autonomous task chaining and multi-step reasoning ⚙️ Full-Stack AI Platforms • Python, Go, TypeScript, React, Next.js, FastAPI, Flask • REST, gRPC, GraphQL microservices • PostgreSQL, MongoDB, Neo4j, Redis • Edge deployment via Docker, Kubernetes, RunPod, Modal, Beam.cloud 🧠 Machine Learning & MLOps • MLflow, DVC, Ray, Kubeflow, Airflow, ArgoCD • End-to-end CI/CD for model training, tuning, testing, and deployment • Serverless GPU pipelines with Triton, ONNX Runtime, DeepSpeed • Multi-model orchestration with autoscaling and cost-aware inference 🔍 Model Engineering & Optimization • Time-series: LSTM, TFT, DeepAR for forecasting and anomaly detection • NLP with GPT-4o, Claude, Gemini, Llama 3 + instruction tuning • Fine-tuning: LoRA, QLoRA, DPO, PEFT, Functionary • Evaluation using Promptfoo, Trulens, Helm for agent scoring and regression • Embedding-based search, classification, clustering, and content tagging 💼 Real-World Use Cases Delivered • Autonomous AI agents for research, documentation, and scheduling • Multimodal copilots (text + vision/audio) using OpenAI Vision, Whisper, LLaVA • Fintech AI for real-time fraud detection, credit scoring, and forecasting • Healthcare agents for intake triage, clinical summarization, and document routing • AI copilots for product QA, internal tools, legal review, and CX automation ✅ WHY HIRE ME 1. Deep technical expertise in today’s most powerful LLMs and ML tools 2. AI-native product mindset — optimized for impact, cost, and user experience 3. MVPs delivered in 6–8 weeks with my proven Starbourne Accelerator 4. Infrastructure built to scale — fast, modular, GPU-ready 5. Clear communication, async-first workflows, and sprint-based delivery 📞 NEXT STEP Click “Invite to Job” or book a quick 15-minute discovery call. Let’s build and launch your AI solution — fast, scalable, and production-ready.

Most AI projects don't fail because of bad code — they fail because nobody checked if the results can actually be trusted. I build and validate intelligent systems: agentic pipelines, LLM-powered workflows, and the evaluation frameworks that make them safe to act on. My work sits at the intersection most engineers avoid: where technical results meet real-world accountability. Recently, my focus has expanded into agentic AI engineering and AI security — designing multi-agent systems with built-in quality loops, hallucination detection, bias auditing, and compliance filters. I don't just build pipelines; I build pipelines that check themselves. I've worked across computer vision, NLP, OCR pipelines, and clinical/academic research — including cell classification, ASR data, and publication-ready statistical analysis. Where I add the most value: — You need an agentic system that produces outputs you can actually trust and act on — You have a model or dataset and need to know if it's reliable before it goes anywhere near a decision — You're writing a thesis, paper, or clinical report and need analysis that survives peer review — Your stakeholders need to understand what the AI actually found — and what it didn't — You need an honest assessment of where your AI system could fail, be gamed, or cause harm I'll tell you honestly if ML isn't the right solution for your problem. That's rarer than it sounds. Core skills: Agentic AI Engineering · LLM Systems & Evaluation · AI Security & Governance · Hallucination & Bias Detection · ML Validation & Evaluation · Healthcare & Academic Data Analysis · NLP · Computer Vision · Statistical Analysis · AI Risk Assessment · Research Reporting Tools & Technologies Agentic & LLM Systems: LangGraph · LangChain · Groq · OpenAI · Pydantic · FastAPI AI Security & Governance: Hallucination detection · Bias auditing · Compliance filtering · Adversarial input testing Validation & Evaluation: MLflow · Weights & Biases · Arize AI · SHAP · LIME · Fairlearn · Great Expectations ML & Deep Learning: Python · PyTorch · TensorFlow · Scikit-learn · Keras Data & Statistics: R · IBM SPSS · Pandas · NumPy · SciPy · Statsmodels NLP & Computer Vision: Hugging Face Transformers · NLTK · OpenCV Data Quality & Pipelines: Great Expectations · DVC · Pandas Profiling Visualization & Reporting: Power BI · Matplotlib · Seaborn · Plotly Cloud & Infrastructure: AWS · GCP · Google BigQuery · Azure

Anyone can put percentages on a profile. Mine come from baselines: I measure the starting point, build a scored test set from real data, and track the same metric after launch. Ask me on a call, and I will show you the measurement behind any number here. My clients cut document search time by 70%, automated 80%+ of tier-1 calls, and drove a $340K pipeline with systems I built. I build production-grade RAG, AI agents, and chatbots on LangGraph, LangChain, n8n, Python, and FastAPI. Built to keep working after launch. A US company runs its daily operations on a multi-agent platform I built, now past 1,000 hours in production. I hold the Claude Certified Architect credential from Anthropic, plus AWS, Azure, and GCP certifications, with 6+ years of ML foundations (Top 100 Global Kaggle Master). Every hour is delivered by me. 𝐏𝐑𝐎𝐎𝐅: 1. Enterprise RAG: Search time down 70%, retrieval precision up 75%, 8,000+ live queries daily. Skill set: Retrieval Augmented Generation, LangGraph, LangChain, Pinecone, pgvector, hybrid search, Ragas 2. Legal LLM, on-premises: Gemma 3 27B fine-tuned on 400K+ documents, 91% citation accuracy, zero hallucinated citations, 60% lower inference cost. Skill set: LLM fine-tuning, Gemma 3, QLoRA, DPO, vLLM, on-premises deployment 3. Voice AI: 80%+ of tier-1 calls handled end-to-end, under 1-second response, handle time down 45%. Skill set: Voice AI, LiveKit, Deepgram, ElevenLabs, Whisper, Retell, Vapi 4. HIPAA clinical AI: Grounded in the hospital's own clinical records, with human review before anything enters the chart. 91% factual accuracy, intake time down 27%, manual documentation down 48%. Skill set: RAG, Claude, PHI handling, AI guardrails, LangSmith, healthcare AI 5. Revenue agents: A multi-agent system that qualifies leads, runs follow-ups, and keeps the pipeline moving while the team sleeps. $340K pipeline in the first 6 months, 1.8x more qualified leads, 48% less manual sales ops, 900+ automated triggers firing daily. Skill set: Multi-agent orchestration, CrewAI, n8n, AI integration, CRM automation 6. Autonomous support: 1 system across 7 channels, handing off to a human the moment confidence drops. 61% of requests resolved end-to-end, average response 44% faster, engagement up 23%. Skill set: Conversational AI, AI chatbot, OpenAI API, n8n, intent routing 7. Longest receipt: 1 scoped task became a platform now serving 3,250+ users, 1,480 hours, and $48,840, all public in my work history below. Skill set: Multi-agent systems, LangGraph, GPT and open-source LLMs, FAISS, PostgreSQL, embeddings, document processing, image OCR 𝐓𝐄𝐂𝐇 𝐒𝐓𝐀𝐂𝐊: 1. Agents and LLMs: LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, OpenAI, Claude, Gemini, MCP servers, function calling, structured outputs 2. Agentic automation: n8n, Make, Zapier, webhooks, REST APIs, event-driven workflows, human-in-the-loop approvals, scheduled agents 3. RAG: Pinecone, Weaviate, Qdrant, FAISS, pgvector, embeddings, semantic search, reranking, GraphRAG, Ragas 4. Fine-tuning: LoRA, QLoRA, DPO, vLLM, Hugging Face, Gemma 3, Llama, Qwen3 32B 5. Voice: LiveKit, Deepgram, ElevenLabs, Whisper, Retell, Vapi 6. Backend: Python, FastAPI, PostgreSQL, MongoDB, Redis 7. Cloud and deployment: AWS Bedrock, Azure OpenAI, Azure AI Foundry, GCP Vertex AI, Docker, Kubernetes, CI/CD, GitHub Actions 8. Observability and evaluation: LangSmith, Langfuse, MLflow, LLM evaluation, golden test sets, AI guardrails 𝐇𝐎𝐖 𝐈 𝐖𝐎𝐑𝐊: I ask about the business before I open the editor, and I tell you when something should stay human. Week 1 ends with the success metric, the baseline, and the test set we score against, agreed before serious money moves. I build in slices you can test and document everything so your team runs the system without me. 𝐅𝐈𝐓 𝐂𝐇𝐄𝐂𝐊: Message me if a workflow is eating hours every week; you can put $1,500+ behind a scoped first milestone, and someone in the room can say yes. Skip me if price is your only scoreboard or production-grade is due by Friday. I filter hard; it is part of why my Job Success Score is still 100%. 𝐂𝐎𝐌𝐄 𝐖𝐈𝐓𝐇 𝐀 𝐏𝐑𝐎𝐁𝐋𝐄𝐌, 𝐋𝐄𝐀𝐕𝐄 𝐖𝐈𝐓𝐇 𝐀 𝐏𝐋𝐀𝐍: Hit Invite or send me a message right here on Upwork with the workflow you want off your plate or the AI build that is misbehaving. You will hear back the same day, whatever your time zone. On the call, you get an execution plan: what the system owns, what stays human, the first metric we measure, and a timeline. The plan is yours either way. Muhammad Ghulam Jillani

𝗔𝗩𝗔𝗜𝗟𝗔𝗕𝗟𝗘 𝗧𝗢 𝗦𝗧𝗔𝗥𝗧 | 🏅 Expert-Vetted AI Developer & AI Engineer | 100% Job Success | 💰 $300K+ Earned on Upwork 9+ Years in AI & Machine Learning | 100+ AI & Data Projects | 2,500+ Hours | 10+ Industries AI Developer | AI Engineer | Machine Learning | Data Visualization | PhD in Artificial Intelligence Hello! I’m Orhan, an AI Developer, AI Engineer, Machine Learning specialist, and Data Visualization expert with 9+ years of experience building intelligent, data-driven, and production-ready software solutions. As an AI Developer, I have delivered 100+ AI & Data Projects across 10+ industries, supporting projects from technical planning and data preparation through development, integration, deployment, and optimization. As a Machine Learning specialist, I improved model performance by up to 35% through feature engineering, model optimization, and stronger data preparation. As an AI Engineer, I reduced data processing and analysis time by up to 60% through automated Python, SQL, and data workflows. Across 9+ years of AI Developer work, I have built Artificial Intelligence applications across Machine Learning, Natural Language Processing, Deep Learning, Computer Vision, Data Science, predictive systems, automation, and AI-enabled software. As a Machine Learning specialist, I improved prediction accuracy by up to 25% across classification, forecasting, and decision-support applications. As a Data Visualization expert, I reduced manual reporting work by up to 70% through automated dashboards, analytical interfaces, and integrated reporting workflows. As an AI Engineer, I support the complete Artificial Intelligence lifecycle across 7+ core stages including data preparation, model development, evaluation, integration, deployment, monitoring, and continued optimization. Across 65 Upwork jobs and 2,500+ hours, I have worked with Python, TensorFlow, Keras, SQL, Git, AWS, APIs, databases, analytical systems, and production software environments. As an AI Engineer, I improved model inference and AI processing speed by up to 40% through architecture optimization and performance improvements. My Machine Learning work covers 7+ core use cases including classification, regression, forecasting, segmentation, anomaly detection, recommendation systems, and pattern recognition. As a Machine Learning specialist, I work across both Supervised Learning and Unsupervised Learning, from data preparation and model selection through training, validation, evaluation, and production integration. My Natural Language Processing work covers 6+ major application areas including text classification, semantic search, information extraction, sentiment analysis, document processing, and language automation. As an AI Developer and AI Engineer, I integrate NLP capabilities with web applications, APIs, internal tools, databases, and operational workflows rather than delivering isolated model experiments. My Computer Vision work covers 4+ core application areas including image classification, object detection, visual recognition, and automated image analysis. As an AI Developer, I have supported AI products ranging from $20K+ image-processing systems to $10K+ AI-powered software platforms, Machine Learning prediction systems, AI automation products, and production analytics tools. As a Data Visualization expert, I have delivered Power BI and analytical dashboard projects valued at $8K+ while supporting real-time reporting, KPI visualization, operational analytics, and decision-support systems. As an AI Developer, Machine Learning specialist, and Data Visualization expert, I reduced time from raw data to actionable insights by up to 50% through integrated Data Analysis, modeling, and visualization workflows. Across 9+ years, I have delivered production-ready Artificial Intelligence work across 6+ major disciplines: Machine Learning, NLP, Deep Learning, Computer Vision, Data Analysis, and Data Visualization. My Data Visualization work transforms Machine Learning and Data Analysis outputs into dashboards, charts, reports, analytical interfaces, and decision-support tools that help teams understand predictions, trends, risks, and operational performance faster. Whether you need an AI Developer, AI Engineer, Machine Learning specialist, or Data Visualization expert, I can support your project from technical planning and architecture through model development, integration, deployment, optimization, and production-ready delivery. AI Developer | AI Engineer | Machine Learning | Data Visualization | AI Developer | AI Engineer | Machine Learning | Data Visualization | AI Developer | AI Engineer | Machine Learning | Data Visualization | AI Developer | AI Engineer | Machine Learning | Data Visualization | AI Developer | AI Engineer | Machine Learning | Data Visualization

I build production-grade AI agents, RAG systems, and computer vision pipelines that ship on real infrastructure — not just demos. IEEE-published AI engineer (DAELocNet, AIIT 2025), Top Rated with 100% Job Success Score. If you need an LLM app that actually works in production — grounded answers, no hallucinations, controlled cost and latency — or a computer vision model running reliably at scale, I take it from idea to deployed system. What I build for clients: AI Agents & Multi-Agent Systems — autonomous, tool-using agents with LangChain, LangGraph, OpenAI & Groq RAG & LLM Apps — retrieval-augmented chatbots with FAISS vector databases, reranking, and hallucination-resistant answers Fine-Tuning & NLP Pipelines — domain-adapted LLMs, text classification, and AI-generated text detection Computer Vision — real-time object detection & tracking with YOLOv8, CLIP, and OpenCV Fintech & Predictive ML — XGBoost fraud detection, repayment prediction (AUC 0.9852), time-series forecasting Rapid AI MVPs — Streamlit dashboards and end-to-end prototypes that get to value fast Recent wins: Built a Binoculars-style LLM text detection system (Qwen2.5) for an AI content verification client Deployed a fintech repayment prediction model achieving AUC 0.9852 with XGBoost + SageMaker Autopilot Built a multi-agent finance automation system using LangChain + Streamlit Real-time object tracking system processing 30 FPS Reduced prediction errors by 40% for an e-commerce client Why clients trust me: BS in Artificial Intelligence (Air University, Islamabad) IEEE-published researcher — DAELocNet presented at AIIT 2025, Riyadh Top Rated on Upwork with 100% Job Success Score End-to-end ownership: data → model → deployment → clean handoff with documentation Core stack: Python · LangChain · LangGraph · OpenAI · Groq · RAG · FAISS · Vector Databases · Hugging Face · Fine-Tuning (LoRA/QLoRA) · YOLOv8 · CLIP · OpenCV · XGBoost · Time-Series Forecasting · Streamlit · FastAPI · PyTorch · Multi-Agent Systems · AI Integration Building an AI agent, RAG system, LLM app, or computer vision pipeline? Send me your brief — I'll outline the best technical approach and a clear path to production.

𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐘𝐨𝐮𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐰𝐢𝐭𝐡 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐑𝐞𝐚𝐝𝐲 𝐀𝐈 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 | 𝟓𝟎+ 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐞𝐝 I am an AI/ML engineer specializing in end-to-end artificial intelligence development — from concept to deployed, scalable systems that solve real business problems. With 8+ years building machine learning, deep learning, and generative AI solutions for global clients (including Huawei and Turing), I deliver reliable, production-grade systems — not just prototypes. 𝐖𝐡𝐚𝐭 𝐒𝐞𝐭𝐬 𝐌𝐞 𝐀𝐩𝐚𝐫𝐭: ✓ Full-stack AI delivery: requirements → architecture → deployment → maintenance ✓ Battle-tested across 50+ international projects ✓ Focus on business impact, not just technical complexity ✓ Clear communication and rapid iteration cycles 𝐂𝐨𝐫𝐞 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: Computer Vision & Image Processing: Object detection | Image classification | ANPR systems | Face recognition | Video analytics | Image segmentation | OCR | Real-time tracking Natural Language Processing & LLMs: Chatbot development | RAG systems | LLM fine-tuning | Text generation | Sentiment analysis | Document summarization | Named entity recognition | GPT/Claude integration Audio & Speech Technologies: Automatic speech recognition (ASR) | Text-to-speech (TTS) | Speaker identification | Audio classification | Voice cloning | Noise reduction 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤:Python | TensorFlow | PyTorch | Keras | Scikit-learn | Hugging Face | LangChain | OpenAI API | FastAPI | Flask | Docker | AWS | Azure 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡:I take full ownership — understanding your use case, recommending the right approach, building the solution, and ensuring it works reliably in production. You get working software, comprehensive documentation, and post-deployment support. 𝐈𝐝𝐞𝐚𝐥 𝐅𝐨𝐫: Custom AI model development and training ML pipeline design and automation API development and third-party integration Proof-of-concept to production scaling Legacy system modernization with AI Let's discuss how AI can create measurable value for your business. Message me with your project details, and I'll respond with relevant examples and a clear path forward.

I am a Machine Learning Engineer with four years of experience working across deep learning research, large scale AI systems, and production model deployment. Over the years, I have worked extensively in medical imaging, computer vision, NLP, signal processing, and large language models, building systems that range from experimental research pipelines to deployed real world AI applications. My day to day work primarily involves Python, PyTorch, TensorFlow, Keras, HuggingFace Transformers, sentence transformers, scikit learn, OpenCV, Pandas, and NumPy. I enjoy working deeply on both the research and engineering sides of machine learning, especially problems that require understanding model behavior rather than simply applying existing architectures blindly. A large part of my background is research driven. I have authored multiple peer reviewed publications in indexed journals and IEEE conferences, including publications in Neurocomputing, Healthcare Analytics, Engineering Applications of Artificial Intelligence, Telematics and Informatics Reports, and other Elsevier and IEEE venues. My research has focused heavily on explainable AI, healthcare AI, and advanced deep learning systems. Some of my published work includes CARDxnosis, an explainable knowledge driven framework for ECG diagnosis and clinical report generation, an explainable AI system for trustworthy arrhythmia detection, a CNN RNN Attention hybrid architecture for automatic modulation classification, ensemble deep learning approaches for lung cancer detection from CT scans, and SRGAN based white blood cell image generation and classification pipelines. Alongside published work, I am currently involved in research on brain tumor segmentation, ADHD and ASD classification from brain connectome graphs, epileptic seizure prediction from EEG signals, and interpretable tabular learning using graph neural networks combined with Kolmogorov Arnold Networks. Beyond research, I have substantial hands on experience building and deploying production grade AI systems. One of my major recent projects was LaborBERT v4, a domain adaptive transformer fine tuning system processing hundreds of thousands of records through a large scale training pipeline. The project involved multiple experimental setups including contrastive learning, masked language model pretraining, temporal contrastive learning, cross attention based fusion, multi task training, and Matryoshka Representation Learning. I have also built hybrid embeddings plus LLM systems for taxonomy mapping using OpenAI embeddings alongside locally hosted LLaMA and Mistral models through Ollama. In addition, I have worked on deployed clinical AI systems and a portable on device diagnostic AI solution with embedded deep learning models for point of care inference, which gave me valuable experience in optimization, deployment constraints, inference design, and production reliability. My broader project portfolio includes vehicle detection using Mask R CNN, human activity recognition on the Kinetics 700 dataset, facial keypoint detection with MultiRes UNet, semantic segmentation pipeline redesign, Stable Diffusion based image editing workflows, toxic comment classification, RASA based conversational AI systems, and large scale scraping and automation pipelines using Playwright and Selenium. I have also worked with Flask and Django based deployment pipelines and cloud hosted ML systems. From an engineering perspective, I care strongly about clean and maintainable systems. I follow disciplined workflows involving modular code design, Git based version control, reproducible experimentation, structured evaluation, bootstrap validated metrics, and detailed documentation. I am also comfortable preparing scientific reports, research papers, and journal submissions using both LaTeX and Word. What ties all of this together is that I genuinely enjoy solving difficult technical problems, especially the kind that require balancing research depth with practical engineering constraints. I am most motivated by projects where thoughtful experimentation, careful system design, and real world usability matter equally.