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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

🤖𝐈𝐟 𝐦𝐲 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐲𝐨𝐮𝐫 𝐚𝐠𝐫𝐞𝐞𝐝 𝐦𝐞𝐭𝐫𝐢𝐜𝐬, 𝐲𝐨𝐮 𝐝𝐞𝐬𝐞𝐫𝐯𝐞 𝟏𝟎𝟎% 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐦𝐨𝐧𝐞𝐲 𝐛𝐚𝐜𝐤. Most businesses hire AI developers to build what they ask for. That's the problem. I don't just build what clients request - I figure out what they actually need first. Usually what they think they need isn't the highest-ROI opportunity. AI-powered solutions delivering production-ready systems measurable ROI and high-impact results - not just prototypes. Work spans finance, healthcare, e-commerce, construction, academia and web platforms, often unlocking $100K–$300K in annual savings 𝐍𝐄𝐗𝐓 𝐒𝐓𝐄𝐏𝐒:- Send me a message with your project problem, budget & timeline. I’ll reply within 24 hours to confirm if I’m the right fit. What Is Built / How Problems Are Solved with AI: ▶️ Multi-Agent AI Systems (LangGraph, LangChain) ▶️ Custom RAG Pipelines (OpenSearch, Pinecone, Supabase) ▶️ Voice AI (VAPI, ElevenLabs, LiveKit) ▶️ Sales & Support Automation (chat + voice) ▶️ Process Automation (n8n, Make, Zapier) ▶️ Full-Stack Development (Node.js, Python, React, Next.js, React Native) ▶️ Dashboards, forecasting models, ETL pipelines ▶️ Chatbots for WhatsApp, Telegram, SMS (via Twilio) My Portfolio Includes: ✔️ Generative AI development: GPT-4o, Claude, Mistral, Llama, Hugging Face, LangChain, LlamaIndex, ChromaDB, Pinecone, Weaviate, Qdrant, Stable Diffusion, Flux 1.1, Ideogram, Lora, n8n, Agentic AI, CrewAI, BabyAGI, AutGen, DeepSeek, Prompt Engineering ✔️ Cost & performance optimization of AI applications ✔️ Time series forecasting models ✔️ ETL & data automation (Airtable, Webflow, Make) ✔️ AI Web development (Django, Flask, Dash with ML models) ✔️ Payment Gateway Integration ✔️ Dashboard development (Plotly Dash, PowerBI, Excel, Looker) ✔️ Data Analytics & Reporting ✔️ Translating business problems to technical teams ✔️ Geospatial Mapping ✔️ Chatbot integration (WhatsApp, Telegram, SMS via Twilio) ✔️ API development & integration ✔️ Research Publications Tech Stack/ Expertise: ⏺️ Python Libraries: Scikit Learn, Pandas, Numpy, Plotly, Tensorflow, Facebook Prophet, Spacy, NLTK, GeoPandas, OpenAi, LangChain, HuggingFace, Matplotlib, Seaborn ⏺️ Web Development: Dash, Django, Flask, FastAPI, Docker, MySQL, MS SQL, Pinecone, HTML5, CSS3, JavaScript ⏺️ MS Office: Excel (Advanced), PowerPoint (Advanced), Word (Advanced), Project ⏺️ Data Visualization / BI: Power BI, Looker, Tableau ⏺️ Front-end Tools: HTML5, CSS3, JavaScript ⏺️ Big Data Tools: PySpark, Hadoop ⏺️ Web-Scraping: Beautiful Soup, Scrapy, Selenium, Playwright ⏺️ Version Control: Git ⏺️ ERP Tools: SAP, Hysabat ⏺️ Cloud Technology: GCP, AWS, Azure, Digital Ocean Why Clients Hire: ⭐ Guidance on what NOT to build (most consultants won’t do this) ⭐ 5.0 rating ⭐ Production systems with metrics, logs, and clean handoffs ⭐ Coordinate senior engineers across ML, data, and cloud ⭐ Clear communication and global availability Let’s build AI solutions that actually work. #AIAgentDevelopment #DataAnalysis #DataEngineering #CloudComputing #PredictiveModeling #MLOps #ImageProcessing #AIChatbot #Python #OpenAIAPI #VectorDatabase #ArtificialIntelligence #RAG #LangChain #Pinecone #MultimodalAI #ProductionDeployment #Deep Learning #Artificial Neural Network #Chatbot Development #Computer Vision #Natural Language Processing #Machine Learning #Artificial Intelligence Ethics #Artificial Intelligence #Chatbot #Machine Learning #Deep Neural Network #Data Visualization #Data Analysis #Information Analysis #Data Science #Data Cleaning #Prompt Engineering #Generative AI #Large Language Model #Granite

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.

If the problem involves AI touching real decisions—and getting it right matters—I’m usually a good fit. I routinely deliver what would take most teams a week in a single day. As one former Upwork client put it: “Got a week’s worth of work done in less than an hour due to Jonathan’s expertise. Would work with him again, no question.” That speed comes from deep domain expertise and fluency with modern AI tooling, which I use as a force multiplier—not a shortcut. I move quickly because I know exactly what to build, what not to build, and how to avoid rework through systematic testing. Most of my work sits in environments where systems influence money, risk, or operations, and where errors are expensive. The goal is not experimentation or novelty. It is building systems that work the first time, explain their decisions, and remain reliable under scrutiny. I led the design and implementation of an AI-driven small-business credit platform for a publicly traded lender (~$40M+ annual revenue). The system automated the majority of application decisions by combining structured financial data, document understanding, and retrieval-augmented analysis with tool-enabled research. Within a controlled environment, the system could query internal data, perform constrained external searches, and assemble a 30-point underwriting profile aligned with senior credit-analyst reasoning. Crucially, the system enforced confidence thresholds and escalation logic. Only the most complex 10–15% of applications were routed to expert underwriters, allowing human effort to concentrate where judgment actually added value. Manual review fell sharply without increasing risk. Security was treated as a first-order constraint. All PII was isolated within private network boundaries, with encrypted storage and tightly scoped access. Even large-language-model interactions—including external research—were routed through secured infrastructure so sensitive data never left controlled environments. Most engagements begin with a short diagnostic phase. I’m direct about whether I’m needed at all—and when I’m not, I say so. Clients often use a less expensive developer for execution once the critical architectural and risk decisions are made. That discipline is part of the job. Selected Systems I’ve Built At Finally, a $100M Series-B payroll company, I designed and shipped a production payroll platform in roughly six weeks, making early architectural decisions that prevented later rewrites and operational debt. In academic publishing, I helped build and stabilize Refine.ink, an AI-driven peer-review platform used by faculty at leading U.S. universities, where rigor, explainability, and predictable behavior were non-negotiable. As Refine.ink’s co-founder put it: “Jonathan combines deep technical ability with unusually strong judgment. He consistently identified the right problems to solve and built systems we could trust in production.” — Ben Golub, Co-Founder, Refine.ink; former Professor of Economics and Computer Science at Harvard; current Professor of Economics at Northwestern University I’ve also built agentic and multimodal systems that execute real workflows: voice-driven legal intake systems that conduct structured interviews and route qualified matters directly into case management; and computer-vision pipelines where I fine-tuned custom object-detection models to classify industrial weld defects—often only a few pixels wide—achieving ~95% accuracy with a lightweight human-review loop. I’m typically brought in when teams need rapid progress without mistakes—when systems have to move fast and still hold up in production. I work quickly because I’m an expert at what I do and deeply fluent in AI tooling, which allows me to compress timelines without cutting corners.

𝗕𝘂𝗶𝗹𝗱 | 𝗗𝗲𝗽𝗹𝗼𝘆 | 𝗦𝗰𝗮𝗹𝗲 You have a visionary SaaS or AI product idea. You don't want to manage a scattered team of backend developers, DevOps engineers, and ML specialists, and you definitely don't want to micromanage tasks. You need a single, accountable technical partner to architect the system, solve the hard problems, and turn your idea into a scalable reality. I’m a Computer Science grad (first-class), but I don't just live in the theory. I combine a heavy academic background in math, stats, and ML with fast, practical execution. I don't just write code. I take ownership of the whole project: the design, the architecture, and the infrastructure. 🧠 𝗛𝗼𝘄 𝗜 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗬𝗼𝘂𝗿 𝗦𝘆𝘀𝘁𝗲𝗺: When you hand me a prototype, an MVP, or even just a wireframe, my first step is mapping the architecture. I am a natural problem solver, so I look at the big picture first. I treat your product like a business asset and focus on the hard realities: 🔹 Can we launch the MVP faster to validate the market? 🔹 Does it do exactly what your users need? 🔹 Is the AWS bill optimized, or are we burning money? 🔹 Will it crash if traffic spikes tomorrow? 🔹 Is your proprietary data actually secure? 🔹 If a server dies, is the database instantly recoverable? 📈 𝗥𝗲𝗰𝗲𝗻𝘁 𝗪𝗶𝗻𝘀 & 𝗪𝗵𝗮𝘁 𝗜 𝗗𝗲𝗹𝗶𝘃𝗲𝗿: 🔹 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 & 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Recently, completely re-architected a system to handle 𝟰𝟬𝘅 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 while achieving a 𝟳𝟬% 𝗰𝗼𝘀𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 on cloud spending. 🔹 𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗦𝗮𝗮𝗦 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: I use my deep learning research experience to build secure, private LLM and RAG pipelines tailored to your business data. 🔹 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 (𝗗𝗲𝘃𝗢𝗽𝘀): I architect zero-downtime CI/CD pipelines and configure AWS/GCP environments so you can ship features fast. 🔹 𝗕𝘂𝗹𝗹𝗲𝘁𝗽𝗿𝗼𝗼𝗳 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: I engineer high-performance backends (Django, Node) that won't crash when your user base scales. 🤝𝗠𝘆 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗦𝘁𝘆𝗹𝗲: I work best with founders and small teams who need a leader. You bring the business problem and the priorities — what matters next and why. I own the rest: the architecture, how the work gets broken down, and the delivery. I run it in focused sprints against agreed goals, with async updates and a call when a real decision needs one — so you get momentum without daily standups eating your week. When you hire me, you are not buying hours of typing, or a developer to assign tickets to. You are buying the judgment, speed, and technical leadership required to win — and a clean, production-ready product handed back to you. Let's look at your system and get it running right.

With a strong background in machine learning, computer vision, and natural language processing, I have consistently delivered real-world AI solutions across diverse industries. My professional journey spans multiple roles where I have developed and deployed intelligent systems for image and language understanding, focusing on accuracy, performance, and scalability. Currently working as a freelancer, I have built intelligent data extraction pipelines for financial applications by combining advanced computer vision and NLP methods to extract information from bank cheques with high precision. Additionally, I worked on a complex chatbot project addressing the limitations of large language models (LLMs), such as context window constraints. I implemented optimization strategies like history truncation, prompt summarization, and prompt caching, which significantly improved coherence, processing speed, and memory efficiency in generated responses. As a Data Science Fellow at Fellowship.AI, I developed a robust evaluation pipeline to measure confidence levels in LLM outputs using datasets like LiveBench and MMLU. I applied strong evaluation metrics such as Expected Calibration Error (ECE) and BERTScore and utilized stronger LLMs for benchmarking, providing valuable insights into model calibration and reliability. Previously, as a Computer Vision Engineer at Hello Llama, I played a pivotal role in building IoT-enabled safety solutions. I developed end-to-end ML pipelines and Android applications integrating BLE and real-time video streaming, designed to work with radar warnings and dashcam systems. My work focused on real-time object detection of urban infrastructure and safety violations, deploying models on NVIDIA Jetson Nano for low-latency, edge-based inference. I also created a custom helmet detection system with facial focus and chin-strap detection and used sensor mat data to recognize multiple riders on scooters. My responsibilities included complete development cycles—from data collection to annotation, model training, validation on unseen data, and deployment—with comprehensive testing to ensure robustness in production environments. Earlier in my career as a Machine Learning Engineer at Expert Consortium Ltd., I worked on automatic face recognition and liveness detection systems. I designed pipelines capable of distinguishing live individuals from photographs and automatically created datasets for unknown individuals by organizing webcam feeds, labeling them, and training recognition models using LBP. I also built a driver activity recognition system for behavioral monitoring and an object tracking tool that captured snapshots when specific spatial triggers were activated. I utilized GPU acceleration and RabbitMQ to ensure high performance and seamless message passing during training and inference. Throughout my experience, I have demonstrated a unique ability to bridge the gap between computer vision and natural language processing. I have built robust pipelines, optimized LLM performance, engineered edge-deployable vision systems, and created intelligent, real-time applications in safety, finance, and mobility. My strengths lie in developing production-ready ML systems, optimizing performance in resource-constrained environments, and delivering intelligent solutions that scale effectively and meet real-world demands. I have direct Experience working on following topics: C++ Python Scikit-learn Tensorflow Keras OpenCv Embedded device Aws Azure Flutter