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**📝 Profile Context / Summary** With 10+ years across enterprise IT and global freelancing, I am an AI-Stack Architect—not just a full-stack developer. In 2026, the industry has shifted decisively: software is no longer just written; it is architected, governed, and continuously learned. The most valuable engineers are no longer those who simply write code, but those who can turn code into cognition—engineers fluent in the AI-stack. I am that engineer. I design and build agentic platforms—systems where AI doesn't just assist but actively plans, builds, tests, and releases software. I have successfully delivered complex solutions across Medical, Fintech, Employee Management, and SaaS industries, handling everything from AI-native frontends to hyper-scalable microservices with autonomous agent orchestration. **🤖 AGENTIC AI & MACHINE LEARNING** 2026 is the breakout year of AI inferencing—where trained models generate predictions and outputs from new data. Most AI computing is now spent on inference rather than training. I specialize in: Agentic AI & Orchestration: LangChain, LlamaIndex, AutoGen, CrewAI—building multi-agent systems that plan, act, and adapt in real time. Full-stack agent platforms combining proprietary training data, planning models, and custom actuation layers. Generative AI & LLMs: OpenAI GPT-4/API, Google Gemini, Anthropic Claude, HuggingFace Transformers. Retrieval-Augmented Generation (RAG) with vector databases (Pinecone, Chroma, Weaviate, FAISS). AI-Native Development: 42% of committed code is now AI-generated, projected to reach 65% by 2027. I don't just use AI tools—I orchestrate AI agents across the entire SDLC: planning, design, build, test, deployment, and maintenance. Deep Learning & Classical ML: TensorFlow, PyTorch, CNNs (Computer Vision), RNNs/LSTMs (Time-Series/NLP), Scikit-learn, Pandas, NumPy. Model Deployment & Optimization: Model compression, quantization, edge computing, cost-per-prediction optimization. AI Governance & Security: AI-generated code carries roughly double the security risk violations of human-written code. I implement robust governance, testing, and security controls for AI systems. **⚛️ MODERN FRONTEND** React remains the most widely used UI library, with 67% of new enterprise React projects now built on Next.js—a 300% increase since 2023. React Server Components are now the default: Core: React 19, Next.js 16 (App Router, Server Components, Turbopack), Remix, Svelte 5 (runes), Astro. Styling: Tailwind CSS, shadcn/ui (1.87M weekly downloads), Framer Motion, Material-UI. State: Zustand (35% adoption, 35ms updates) vs Redux (38% adoption, 65ms), TanStack Query, Jotai. Build Tooling: Vite 8 with Rolldown (Rust-based, 10-30x faster builds), Turbopack. TypeScript: Used by 38% of professional developers, required in 72% of frontend job postings. **📱 MODERN MOBILE** React Native's New Architecture (stable since 2024) delivers significant performance improvements through synchronous native module calls: Frameworks: React Native (Expo), NativeScript, Flutter. Native: iOS (Swift/SwiftUI), Android (Kotlin & Jetpack Compose). Emerging: GPU-powered rendering for consumer and enterprise apps. **⚙️ MODERN BACKEND & API** Runtimes: Node.js (NestJS, Express), Bun, Deno, Python (FastAPI—57.9% developer usage, up 7% from 2024), Java (Spring Boot 4.0—modularized, Java 25 LTS support). Spring AI 1.0: Built-in support for integrating LLMs and AI services into enterprise Java applications. API Design: RESTful, GraphQL (Federation), gRPC, Webhooks, WebSockets. Event-Driven: Apache Kafka, RabbitMQ, AWS SNS/SQS. **☁️ DEVOPS & CLOUD (2026 Standard)** Cloud-native and Serverless adoption has surpassed 70% penetration in enterprise IT. Multi-cloud is now the standard—Gartner projects over 75% of cloud customers will adopt this model: Cloud: AWS, GCP, Azure (multi-cloud architecture). Containerization & Orchestration: Docker, Kubernetes (K8s), Helm. Serverless: AWS Fargate, Lambda, Cloudflare Workers—extending to data pipelines, real-time streaming, and event-driven microservices. IaC: Terraform, AWS CDK, Pulumi. CI/CD: GitHub Actions, GitLab CI, ArgoCD, Jenkins. **🗄️ DATABASES & SEARCH** Relational: PostgreSQL (PostGIS), MySQL, SQLite. NoSQL: MongoDB, DynamoDB, Firebase. Vector (AI): Pinecone, Chroma, Weaviate (for RAG and semantic search). Search: Elasticsearch, Algolia, Meilisearch. Caching: Redis, Memcached. **🔗 CRM, AUTOMATION & INTEGRATION** In 2026, the CRM market is consolidating around Salesforce, Microsoft, ServiceNow, and HubSpot as a durable challenger. Both platforms have invested heavily in agentic AI, predictive intelligence, and conversational AI: CRMs: Salesforce (Apex), HubSpot (Agentic Engagement Object), Zoho. Low-Code/No-Code: Gartner forecasts the low-code market at $44.5 billion in 2026, with 75% of new enterprise apps built on low-code platforms. Automation: VBA, Excel Macros, Google App Script, Bubble.io, n8n, Make, Zapier. Citizen Development: 80% of low-code users are now "citizen developers". **🚀 Why Partner With Me in 2026?** The software industry has crossed a clear threshold in 2026. Generative AI is no longer just helping developers write code faster—it is reshaping how software is planned, built, tested, and delivered. The role of the developer has evolved from coder to curator of intent, constraints, and outcomes. **I bring:** 10+ years of battle-tested engineering across the full spectrum. Agentic AI fluency—the ability to orchestrate AI agents across the entire SDLC. Systems thinking and architectural judgment—skills that AI cannot replace. Security-first mindset—AI amplifies what's already there; where code quality is managed, AI accelerates delivery; where it isn't, it accelerates technical debt and security exposure. Whether you need an AI-native application, a microservices overhaul, an agentic workflow orchestration, or an intelligent CRM integration—I deliver professional, scalable, and governable solutions. Let's architect intelligence together. **Contact me today.**
Business Intelligence Engineer with 10+ years of Looker/LookML experience and 1 year of Tableau experience. Former data scientist, web developer, and operations analyst. I'm happy to help you with building analytics solutions, whether it's just some quick questions or a longer consulting arrangement. I have worked within FAANG teams and other big tech organizations and have a great understanding of how things *should* work when the right structures are in place. I've seen enough examples of things breaking to have a good "sixth sense" with troubleshooting and take pride in my ability to solve urgent issues.
AWS Certified Solutions Architect and MLOps / DevOps Engineer with over 9 years of experience designing, deploying, and operating secure, scalable cloud and AI-driven platforms across AWS and Azure. Proven expertise in containerization and orchestration with Docker and Kubernetes (EKS, AKS, OpenShift), enabling high-availability, multi-cluster deployments and large-scale application modernization. Strong background in building end-to-end CI/CD pipelines using GitHub Actions, Terraform, and Helm to drive automation across infrastructure, application delivery, and machine learning workflows. Extensive hands-on experience in Python development, building production microservices with FastAPI and Flask, designing robust API architectures, and supporting real-time applications with WebSockets and streaming frameworks. Skilled in establishing MLOps pipelines, deploying and monitoring ML models in production, and optimizing GPU-based workloads for performance and cost efficiency. Experienced with vector databases, embedding-based systems, and large-scale PostgreSQL environments. Deep understanding of cloud security, observability, and disaster recovery planning.
I'm a senior data scientist with nine years of experience taking ML systems from idea to production, specialising in Large Language Models, RAG and graph analytics. I recently completed a contract at the UK's Financial Conduct Authority, building graph analytics on AWS Neptune to help uncover hidden financial-crime networks. Before that, at Phaidon International, I designed and deployed Bullseye, an AI candidate-matching system (Neo4j graph of 2M+ candidates, OpenAI embeddings, LLM reranking) that became the firm's primary recruitment technology, used by 100+ recruiters across three countries. I hold an MSc in Computational Statistics and Machine Learning from UCL, with thesis research on link prediction in criminal networks, first applied during a placement with the UK Home Office's organised crime division. I enjoy making complex things easy to understand. Whether you need help debugging Python, designing an ML pipeline, integrating LLMs or RAG into your product, or getting a graph database to behave, I can help.
1. **AI apps development (chatbots, content automation, decision making)** My tech stack is: - Python - OpenAI API (GPT-3.5, GPT-4) - LangChain - Pinecone - Flask/Streamlit - Git - Docker 2. **Prompt Engineering** I've dedicated 800+ hours this year alone writing prompts and teaching it to others. ------ I'm an entrepreneur and coder with experience in marketing (19y) data science (10y) and AI (1.5y). Some of my AI work: 1. An LLM app that collects, overviews and asnwers questions about AI news and other AI related content. The tool also creates social media content based on these overviews for me. https://www.youtube.com/watch?v=0fDhtJ8PxLk 2. Prompt Engineering case-study: https://www.notion.so/valueto/Prompt-Engineering-Case-Study-collecting-categorising-and-repurposing-content-by-Tony-AI-Champ-43354a95777a49fb90e1f35c5385ca1d 3. Airi Onboardwell, an AI conversational chatbot. In this instance, Airi is trained on data about a social media role in the company named InsightWhale (that serve web3 clients) to help new employee on this role onboard faster and save InsighWhale 1 salary out of 6 that a company normally spend on onboarding a new employee. You can ask her questions about the role details. Airi is trained the way that if she doesn't know the answer, she will raise the question to the relevant person in the company, get an answer and memorise it so that next time a similar question emerges, she can answer it. https://aiemploy-ee-airi-onboardwell-flask-sg.onrender.com 4. My Udemy prompt engineering course with over 1700 students with an average rating of 4.5/5: https://www.udemy.com/course/chatgpt-for-data-science-and-data-analysis-in-python/
Data engineer | Machine Learning Engineer | Software engineer with proven record of 10 years in building data strategy and delivering ML/AI and data solutions
AI Systems Architect, GTME, CSA, SRE, and Fractional CTO helping organizations leverage custom AI, autonomous agents, and cloud infrastructure to build products faster, automate operations, and scale efficiently. Expert in custom AI model development, fine-tuning, RAG implementations, multi-agent systems, AI-powered workflow automation, LLM integration, cloud architecture, cybersecurity, and high-availability SaaS operations. With decades of hands-on engineering experience and deep expertise in modern AI tooling, I bridge the gap between traditional software engineering and next-generation AI systems. I build production-ready AI solutions that deliver measurable business outcomes—not demos, prototypes, or hype. From custom AI platforms and autonomous agent ecosystems to cloud-native SaaS infrastructure and mission-critical production environments, I help organizations deploy secure, scalable systems that create immediate operational leverage. Extensive NDA obligations limit what I can publicly share, but my experience spans startups, growth-stage companies, enterprise organizations, and large-scale SaaS platforms operating under demanding uptime, privacy, and security requirements.
** Full refund if I fail to solve the problem.** Experienced Software Architect, Data Scientist and a polyglot programmer with 9+ years of experience in writing production software code using NodeJS, Go, C# .NET, Python, R, JavaScript, Java, C/C++, and others . In my stint at Research Lab, I worked on building Chatbots, Augmented Reality powered apps, Data Science and Machine Learning. I love explaining complicated things in the most simple way.
Certified in UiPath, Agentic AI and Microsoft Power Platform, with 15 years of IT experience in analysis, design, development, integration, testing, and maintenance . Proven expertise in designing and implementing end-to-end Robotic Process Automation (RPA) strategies for web, desktop, mainframe, PDF, SharePoint, and Excel applications using UiPath, integrated with Microsoft and Google OCR technologies. Delivered 200+ UiPath RPA bots globally, with major projects for US clients, ensuring on-time, quality delivery. Skilled in Microsoft Power Platform – including Power Automate Flows and Power Automate Desktop. Hands-on experience in LLM, RAG, Prompt Engineering, Agentic Automation, and AI Agents.
I have total 10+ year experience in data science, machine learning, python, NLP. I am expert in GenAI, Agentic AI, AI Agents, Langchain, LangGraph, CrewAI, Mutli Agent AI, LLM, Prompt, statistics analysis, regression, classification, recommendation, prediction, NLP, data analysis, feature engineering, python programming.
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