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High-quality datasets are the foundation of powerful AI models. I provide pixel-perfect computer vision annotations and structured data analysis to ensure your models train on flawless inputs. With extensive experience across leading platforms like CVAT and V7 Darwin, I deliver high-precision, production-ready training data under tight deadlines. Core Capabilities: * Computer Vision: Semantic & Instance Segmentation, Polygons, Precise Masking, Bounding Boxes, and Chroma Keying. * Audio & Text: High-accuracy Transcription, data cleaning, and linguistic formatting. *Data Analytics: Data preprocessing, quality assurance, and anomaly tracking to optimize your pipelines. Tool Stack: Annotation: CVAT, V7 Darwin, Labelbox, Roboflow. Media & Audio: Transcription software, Chroma Key tools, Bounding boxes, segmentation Analysis: Excel/Google Sheets Why work with me? > I maintain a 98%+ pixel accuracy standard, strictly follow complex labeling schemas, and scale efficiently based on your project guidelines.Let's discuss your dataset requirements. Click "Invite to Job" to get started!

I am not just an AI Engineer; I am a storyteller who connects the dots between complex data and business growth. With 5 years of hands-on experience and a robust academic foundation in Statistics and Engineering, I specialize in building AI systems that don't just work they innovate. Why work with me? I don’t just deliver code; I translate your high-level business needs into high-performing, production-ready AI systems that solve real-world bottlenecks. My Core Expertise: - AI Solutions: Text analysis & image recognition - AI Search: Smarter answers with RAG & advanced prompt design - Custom AI Models: Tailored GPT, Gemini, LLaMA, Claude & more - Vibe Coding: Cursor, Lovable, Antigravity, etc.. - AI Workflows: Multi-agent automation for complex tasks - Voice AI: Text-to-speech & speech-to-text (AWS, Google, Azure) - AI Visuals: From idea to image using DALL·E, Midjourney, Stable Diffusion - Automation: Zapier, Make, n8n & custom workflows - Smart Pipelines: Event-driven triggers, error handling & smooth operations AI Agents & Chatbots: I build sophisticated multi-agent and RAG frameworks. Examples include E-commerce virtual associates that drive sales and POS customer support agents that handle complex queries autonomously. Text-to-SQL & Analytics: I enable non-technical users to "talk to their data," providing instant, natural-language insights into sales, inventory, and KPIs. Intelligent Automation (n8n): I streamline operations by eliminating repetitive tasks. My AI-powered HR Agent workflow automatically parses, scores, and ranks candidates to find your "best fit" instantly. Computer Vision & OCR: Expert in YOLO and Qwen2.5-VL. I automate data entry from handwritten or digital invoices directly into structured JSON for accounting and inventory software. Full-Stack AI Deployment: I take models from notebooks to production. Expert in the full AI lifecycle, including MLOps, containerization (Docker), and scalable cloud deployment on GCP. The Toolbox: Frameworks: PyTorch, Keras, TensorFlow, Scikit-learn, OpenCV. LLM Ops & Orchestration: LangChain, LangFlow, DSPy, OpenAI API, Apple MLX. Deployment: Docker, GCP, MLOps pipelines. I am dedicated to delivering results that exceed expectations always on time and within budget. Let’s build your success story. Click the 'Invite' button to start a conversation!

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 take over AI systems that are already running in production - and make them survive scale. Most AI work is greenfield: build a demo, hand it over, walk away. My work is the harder half - inheriting a system that real clients already depend on, understanding why every decision was made, and extending it without quietly breaking what works. WHAT I BUILD Multi-agent pipelines that do real operational work. My most recent build was a twelve-agent outbound system: it enriches leads from raw domains, generates personalised assets per prospect, drafts sequenced copy in the client's voice, and runs an automated QA gate so nothing generic ships. I know where these break as you scale - silent enrichment failures, prompt drift once you onboard clients with genuinely different voices, and QA rubrics that keep passing weak output because they were written for client #1. Custom MCP servers. I build Model Context Protocol servers in Python, Node and TypeScript, so your CRM, project tracker and knowledge base become native callable tools for Claude - instead of brittle Zapier chains that fail silently. RAG systems that stay accurate. Retrieval pipelines with a deliberate chunking strategy, evaluation, and cost control - routing the right work to the right model rather than sending everything to the largest one. Migrations and rescue work. Moving systems off vendor AI gateways onto direct API integrations, porting between providers, and deploying to Cloudflare Workers, Azure and GCP. HOW I WORK Every system I build ships with documentation and an SOP. Not as a nice extra - a system only one person understands is a liability, and my job is to leave you something your team can actually operate. I've authored a 52-week AI engineering curriculum, so explaining architecture to non-technical stakeholders is a format I'm comfortable in. I flag problems early, including in my own work. If something I built isn't performing, you'll hear it from me first. STACK Claude (Opus, Sonnet), OpenAI, Gemini · LangChain, LangGraph, CrewAI · MCP servers (Python / Node / TypeScript) · Pinecone, Qdrant, pgvector, Supabase · n8n, Make, Zapier · FastAPI, Node, React, Next.js · Cloudflare Workers, Azure, GCP, Docker GOOD FIT IF YOU - Have an AI system in production that needs an owner, not a rebuild - Want agents doing operational work, not a chatbot demo - Value documentation and handover as much as the build itself PROBABLY NOT A FIT IF YOU - Need a fixed-scope $200 script - Want someone to execute tickets without questioning the architecture Tell me what's currently breaking, or what you're trying to automate, and I'll tell you honestly whether I'm the right person for it.

Your AI agent works in the demo. Then it meets real customers, real documents, real edge cases, and it breaks. Closing that gap is what I get hired for. I build AI agents, RAG systems and LLM automation that hold up in production, and I've shipped them for companies in the US, UK, Canada and Brazil. WHAT I BUILD AI agents and multi-agent systems. Autonomous workflows on LangGraph, AutoGen and LangChain that plan, call tools and finish real tasks instead of just talking about them. RAG and document intelligence. Pipelines that read contracts, invoices, specs and business documents, pull out the fields you need, and flag the inconsistency a human would have missed on page 40. LLM integration and automation. Wiring OpenAI, Claude and open source models into the products, CRMs and internal tools you already run. Evals and reliability. LLM-as-judge scoring, guardrails and regression tests, so you know when your agent is right instead of hoping it is. Production backends. FastAPI services, vector search (Qdrant, pgvector), caching, queueing, deployment, monitoring. RECENT WORK Document intelligence for a US analytics company. Ingests engineering and business documents, extracts the critical fields, surfaces inconsistencies for review, and replaces work that was being done by hand. A multi-agent workplace assistant on AutoGen, with a tool layer across Slack, Notion and Microsoft Teams. Users run projects by typing what they want. An LLM interview platform with a live evaluation loop that scores answers on quality, clarity and relevance and returns coaching, personalized per user through RAG over uploaded resumes and job descriptions. An agentic operations platform for restaurant operators: procurement monitoring, demand forecasting, live analytics. A generative backend on FastAPI for a UK fintech that resolves complex queries through dynamic tool execution. STACK Python, FastAPI, LangChain, LangGraph, AutoGen, MCP, OpenAI, Anthropic Claude, LLaMA, Groq, Hugging Face, Qdrant, PostgreSQL, Redis, Celery, Docker, AWS, GCP, Azure, Next.js, TypeScript HOW I WORK I work solo. You deal with me. No account manager, no subcontractor, no handoff to someone you never interviewed. I scope before I build. If your brief is wrong or missing something, you hear it from me before you pay for it, not after. I ship systems that run, not demos that impress. If you have an AI agent that needs to work in production, or a half-built one that doesn't, tell me what it's supposed to do and I'll tell you what it actually takes.

Availability: Full-time freelancer, 𝟰𝟬+ hours/week, open to long-term collaborations. I’m a Full-Stack & AI Engineer with 10+ years of experience building web and mobile applications and 3+ years of specialized experience in AI and Large Language Models (LLMs). I design, develop, and deploy production-grade platforms, from scalable SaaS dashboards to AI-powered assistants, RAG systems, and voice agents. I work end-to-end: architecture → backend → frontend → cloud deployment, with a focus on clean code, maintainable systems, and high performance. Over the past few years, I’ve delivered solutions that integrate AI/LLM pipelines, vector search, real-time chat, and voice agents for enterprise and startup clients. 🤖 AI & LLM Expertise - MCP Server Development: Designing and integrating custom MCP servers for AI agents, enabling structured tool usage, external system integrations, database querying, and API orchestration. - Fine-Tuning: Persona creation, Q&A systems, and domain-specific models (medical, legal) using Mistral and Llama 3. - Synthetic Dataset Generation: Streamlining LLM training with high-quality datasets. - Evaluation Frameworks: Assessing LLM performance with custom metrics. - Cloud Deployment: Deploying LLMs on AWS and GCP. - AI Agents & Voice Bots: Proficient with LiveKit, Retail AI, OpenAI. - Open-Source Deployment: Expertise deploying models like vLLM on AWS/GCP/RunPod using SkyPilot. 🛠️ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗧𝗼𝗼𝗹𝘀 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 ➜ LLM Tools: LangChain, Langsmith, Langfuse , Hugging Face, Transformers. ➜ Vector Databases: Chroma, FAISS, Pinecone, Qdrant , Opensearch ➜ AI Workflows: Flowise AI, LangFlow, StackAI. 🛠️ 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 ➜ Languages & Frameworks: Python, Node.js, ReactJS. ➜ Database Management: MongoDB, MySQL, PostgreSQL , Supabase , FIrebase ➜ Frontend & Backend Integration: Seamlessly connecting APIs and user interfaces. 🌟 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 ➜ Open-Source LLMs: Proficiency in LLAMA 3, Mistral 7B, and Mixtral 8x7B. ➜ Prompt Engineering: Expertise in techniques like Chain of Thought, Few-shot Prompting, and Self-Reflection. ➜ Fast Inference: Implementing high-speed solutions with vLLM . 🌟 𝗪𝗵𝘆 𝗖𝗵𝗼𝗼𝘀𝗲 𝗠𝗲? With over 10 years of experience, I deliver scalable, cutting-edge solutions tailored to your project’s needs. Whether it's advanced AI models, MCP server development, LLM optimization, or full-stack development, I ensure top-notch results every time. Let’s collaborate to bring your ideas to life!