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AI Agent and RAG systems that reach production, not just demos. I have built LangGraph pipelines, multi-agent systems, and LLM apps deployed on real infrastructure for clients in insurance, recruitment, SaaS and e.t.c. In 2 years across Readfly and Physar Agency, I have shipped: • A hybrid RAG retrieval system using LangChain and OpenAI embeddings for a job-matching platform, cutting candidate search time by over 60% • Predictive ML models for insurance premium pricing using XGBoost and SHAP for explainability, deployed via FastAPI with full MLflow tracking • End-to-end data pipelines automating ingestion from Gmail and SharePoint into Supabase, used in production daily What I build for you: Agentic AI Systems: Multi-agent workflows built with LangGraph and LangChain, agents that plan, retrieve, decide, and act across your tools and APIs. RAG Pipelines: Production retrieval systems using Pinecone, ChromaDB, and semantic search, so your LLM answers from your data, not hallucinations. LLM Applications: Chat interfaces, document Q&A tools, SaaS AI features, built with FastAPI backends, clean APIs, and deployable from day one. ML and Explainable AI: XGBoost, LightGBM, and SHAP-powered models for pricing, classification, and forecasting, with explainability reports for stakeholders who need to understand the output, not just the score. MLOps and Deployment: MLflow experiment tracking, Docker containerisation, CI/CD pipelines, and A/B testing setup, so your model stays reliable after it ships. What makes me different: Most AI engineers hand over a notebook. I hand over a running system. • I design for production from the first line, scalable architecture, clean APIs, monitored deployments • I include drift detection and evaluation (RAGAS for RAG, A/B testing for models) so you know when your AI degrades • I work with MLflow and CI/CD so updates ship without breaking what works • I communicate in plain English, not model metrics your team cannot act on Tech stack: Agentic AI: LangGraph, LangChain, Multi-Agent Systems, Prompt Engineering Vector and Retrieval: Pinecone, ChromaDB, Semantic Search, RAGAS Evaluation ML and Explainability: XGBoost, LightGBM, SHAP, NLP Deployment and MLOps: FastAPI, Docker, MLflow, CI/CD, Drift Detection, A/B Testing Cloud and Data: Azure, MongoDB, PostgreSQL, SQL, Python If you are building an AI product, adding intelligence to an existing platform, or need a system that actually ships, send me a message. I will respond within 2 hours and tell you exactly how I would approach your project.

⚡ I build production-ready, AI-powered web applications — from model training to fully deployed web products. With 7+ years of hands-on experience across full-stack development and AI/ML engineering, I specialize in one thing most developers can't offer: the full picture. I don't just integrate an API and call it "AI" — I architect robust, scalable systems where the AI layer genuinely solves your business problem. If you need a freelancer who can handle the entire stack — AI/ML backend, REST APIs, and polished frontend — you've found your person. What I build for clients: ✅ ML model development, fine-tuning & deployment (PyTorch, TensorFlow and more) ✅ LLM API Integration. GPT, Claude, Gemini integration into existing SaaS, CRMs, ERP & Web apps ✅ RAG systems — "chat with your documents" (PDFs, databases, wikis) ✅ AI agents & autonomous workflow automation (LangChain, CrewAI, LangGraph ✅ Custom AI chatbots & LLM-powered apps (GPT, Claude, Gemini, Llama) ✅ Full stack web applications (React/Next.js + Python/FastAPI/Django) ✅ Data pipelines, annotation systems & MLOps infrastructure ✅ Cloud deployment AWS, Google Cloud, Azure, Digital and many more ⚙️ Tech Stack at a glance: → AI/ML: Python · PyTorch · TensorFlow · Scikit-learn · HuggingFace · OpenAI API · Anthropic API → LLM Frameworks: LangChain · LlamaIndex · LangGraph · CrewAI → Vector DBs: Pinecone · Weaviate · ChromaDB · FAISS → Backend: FastAPI · Django · Node.js · PostgreSQL · MongoDB → Frontend: React · Next.js · TypeScript · Tailwind CSS → Cloud/MLOps: AWS · GCP · Docker · GitHub Actions · SageMaker 💡 Why Clients Choose Me: Most ML engineers can't build a polished, deployable product. Most full-stack developers don't truly understand AI systems. I bridge both worlds — and I've done it for 7+ years. You get a developer who: → Understands your business problem first, code second → Delivers working systems, not just demos or prototypes → Communicates clearly throughout — no technical black boxes → Has seen enough projects to know what breaks in production I work with startups, SMBs, and enterprise teams across the US, UK, EU, and Middle East. Whether it's a greenfield AI product or adding intelligence to your existing platform, I bring the same rigorous engineering approach. I focus on outcomes that matter: systems that save you time, reduce costs, or generate revenue. 📩 Message me with your project details — let's build something great together. #FullStackDeveloper #MERNStack #AIDevelopment #OpenAI #AIChatbot #Automation #Microservices #AWS #ML Engineer

I build production AI systems that businesses run on every day: AI agents, RAG pipelines and LLM workflow automation for companies where a wrong answer costs real money. WHY CLIENTS PICK ME • Expert-Vetted, the badge Upwork awards its top 1% by interview, not by algorithm. 100% Job Success across 70+ projects and six years. • Production, not prototypes. Not proof-of-concepts. Every system ships with schema validation, evals and a regression suite, so it still works six months after launch and you can prove it. • Scale. I have processed 12 million documents through a single RAG pipeline and run agents live in front of real consumers. • I integrate with what you already have. Your CRM, your database, your APIs, your cloud. AWS certified (ML Specialty), equally at home on GCP. • You always know where the project is. A written update whenever something moves and a weekly Loom walkthrough. Clients tell me this is the part they remember. WHAT HAPPENS WHEN THIS GOES WELL A process that eats your team's week runs on its own, and you get the evidence it did not get worse. On an AI underwriting platform used by 15+ insurers across the US, policy review went from days to 3 to 5 minutes at 99.55% precision on the rules that move money. A UK mortgage broker cut manual payslip review by 80%, because the automation only escalates the documents two models disagree on. A UK government-data client got 12 million planning documents extracted, embedded and searchable in under 48 hours at 65% below the GPT-4 baseline cost. Most of my engagements run long-term. The first project automates one workflow; the ones after that tend to automate the rest. WHAT I DO 1. AI agents and workflow automation. Multi-agent systems that take a manual process end to end and make real decisions along the way. Built in LangGraph and Claude with MCP, schema-validated, observable at every step, with a human in the right place by design. I built the assistant inside a UK consumer money app: seven specialised agents, 43 deterministic tools, every financial figure computed by code rather than the model, so it cannot make up a number. 2. RAG and LLM engineering. Retrieval pipelines, knowledge assistants and chatbots over your own data, and cost and accuracy work on LLM systems already in production. 700+ UK planning policy documents summarised weekly, fully automated, at 75% lower LLM cost through context caching and batch APIs. 3. The machine learning and data foundations underneath. Document and data extraction, classification, Python, SQL, pandas and scikit-learn. A private-equity deal-screening agent I built completes the firm's own investment scorecard end to end, backtested against realised fund returns. HOW I BUILD The simplest thing that works, then iterate with evidence. Strong prompting before fine-tuning. An API call before custom infrastructure. A benchmark before an architecture decision. Non-negotiable on every build: • Structured outputs with schema enforcement. If it does not validate, it does not pass • Dual-model verification on high-stakes data • Full observability with LangSmith or Langfuse. Nothing is a black box • A gold-standard eval set built early and regression-tested on every change WHAT CLIENTS SAY "His Loom updates were the highlight of my week! He's great at communicating complex AI concepts to non-technical people and keeps you in the loop every step of the way." Chris Barnes, Co-Founder, Gains App "Adam is an absolute powerhouse of an LLM Engineer. He has first class communication skills which make working with him an absolute pleasure." Sammie Ellard-King, Founder, Gains App "A great balance of personality, professionalism and a deep knowledge of the AI space. He's a strategic thinker who considers the bigger picture." Tom Story, PlannrAI STACK • Agents and orchestration: LangGraph, LangChain, Claude Code, MCP, LangSmith, Langfuse • Models: Claude, OpenAI, ChatGPT, Gemini, Llama, via Bedrock, Vertex and direct APIs • Backend and infrastructure: Python, FastAPI, Postgres with pgvector, Docker, Kubernetes, AWS (ML Specialty certified), GCP • Machine learning: PyTorch, scikit-learn, pandas, SQL • Reliability: Pydantic structured outputs, dual-LLM verification, evals and LLM-as-judge test suites TAKING ON NOW • AI agent and workflow automation builds, end to end • RAG pipelines and knowledge assistants over your own data • Cost and accuracy work on LLM systems already in production • Claude Code and AI engineering enablement for teams adopting AI Expert-Vetted · 100% Job Success · $400K+ earned · 5,750+ hours · 70+ projects

I specialize in applying advanced computational techniques to solve complex scientific problems. With 12 years of experience in software development and academic research, I have honed my skills in fields like Bayesian inference, machine learning, and physics simulations. My background includes roles as a Senior Researcher at Microsoft Research and as a Research Fellow at a Flatiron Institute, where I dove deep into interdisciplinary projects involving molecular dynamics and image processing. Proficient in Python, C/C++, CUDA, and parallel programming, I am committed to delivering innovative solutions that drive results. If you're looking for a collaborator to tackle challenging research or development projects, I am eager to discuss how my expertise can help elevate your work.

I help organisations turn AI ideas into secure, scalable, production-ready solutions. I am an Expert-Vetted AI consultant, AI leader and PhD-qualified machine learning specialist with more than 12 years of experience delivering AI, machine learning and generative AI solutions across enterprise, startup, healthcare, education and technology environments. My work covers the full AI lifecycle—from strategy and solution architecture through rapid prototyping, production deployment, MLOps, governance and adoption. How I can help ▪ Generative AI and AI agents RAG applications, enterprise copilots, agentic workflows, document intelligence, LLM evaluation and secure deployment. ▪ AI strategy and technical advisory AI roadmaps, use-case prioritisation, architecture reviews, build-vs-buy decisions, vendor evaluation and fractional CTO support. ▪ Machine learning solutions Prediction, recommendation, personalisation, classification, forecasting and optimisation systems. ▪ Cloud AI architecture Production-grade solutions using Google Cloud, AWS and Azure, including Vertex AI, BigQuery, Cloud Run, SageMaker, Bedrock and associated data services. ▪ MLOps and LLMOps Automated training and deployment pipelines, monitoring, evaluation, model governance, CI/CD and responsible AI controls. Selected outcomes - Built and led an AI and data science function, growing the team from 3 to 15 people - Delivered AI initiatives generating millions in measurable value - Deployed generative AI solutions that improved operational efficiency by 35% - Developed machine learning systems that increased enrolment by 6% and associated revenue by 10% - Reduced model deployment time by approximately 50% through reusable MLOps frameworks - Supported startups as a fractional CTO, helping convert early-stage concepts into working MVPs and investor-ready technical roadmaps Recognition - DataIQ Future Leader 2025 - Winner, HESPA Innovation Award 2025 - Finalist, DataIQ Most Innovative Use of AI in Europe - Endorsed as a Data Science Leader by the Royal Academy of Engineering I combine hands-on technical depth with executive-level communication. I can work directly with engineers and data scientists, while also translating complex AI decisions into clear commercial recommendations for founders, directors and senior stakeholders. Typical engagements include AI discovery workshops, architecture design, GenAI prototypes, production implementations, technical due diligence, fractional AI leadership and ongoing advisory support.

Hi, I am a senior AI Engineer specialising in Computer Vision and I hold a PhD in a related field . I have both industry and academic research experience in deep learning and general software development. My services include: ✔️Training neural networks from scratch in any framework you like ✔️AI consultancy ✔️Building more optimal AI models and pipelines ✔️Identifying use cases for AI in your business and implementing it if needed ✔️Any computer vision-related problem solution ✔️Writing academic reports and articles on existing machine learning architecture. ✔️Data Scraping, Web Bot development ✔️Data Labeling