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GIS Analyst with +4 years of experience and a strong academic background in Earth Science and Environmental Engineering, complemented by professional experience in the renewable energy sector. I specialise in delivering practical, data-driven solutions to complex geospatial and environmental challenges. My expertise includes: 📊 Environmental Management, Monitoring, Impact Assessment and Reporting 🌍 Geospatial Analysis 🛰️ Remote Sensing 🗺️ Cartography & Map Production 🐍 Python I am dedicated to delivering high-quality, efficient solutions with a strong analytical and organisational approach. Let's discuss how I can contribute to your next project! Feel free to reach out, I look forward to collaborating!

I build intelligent systems — not scripts, not one-off fixes, not half-baked automations. If you need someone to think, architect, integrate, and deliver a system that genuinely improves your business, this is where I operate. I specialize in designing and implementing AI-powered automation ecosystems that combine: • Python & FastAPI for custom backend logic • n8n to orchestrate workflows with reliability • API integrations (Stripe, Binance, Supabase, HubSpot, Notion, ClickUp, custom CRMs) • LLM agents for insights, analysis, decision-making • Financial & trading logic (execution engines, risk management automation) • Dashboard systems for clarity (React, Tailwind, shadcn) What I Actually Build • Real-time execution engines (trading, financial, operational) • Automation systems that replace full teams • AI agents that deliver insights and decisions weekly • Data pipelines + workflow orchestration • Intelligent dashboards for business clarity • End-to-end custom platforms (FastAPI + React) Why Clients Hire Me Because I don’t just “build tasks.” I design the system behind the system. You get: • architecture • reliability • long-term design • clarity • communication • no bullshit execution Technical Stack Python, FastAPI, PostgreSQL, Supabase, SQLAlchemy n8n, Zapier, Make React, Next.js, Tailwind, shadcn/ui OpenAI, local LLMs, agent frameworks CCXT, Binance API, execution pipelines Docker, Linux servers, CI/CD If you need: • a system that makes money • a system that saves money • a system that gives clarity • or a system that runs without humans I’m the person you build it with. Send me a message with your objective and I’ll tell you what’s possible.

I build complete products end to end, solo: PostgreSQL schema, typed API (tRPC), React or React Native frontend, AWS infrastructure as code, keyless CI/CD, custom domain, and TLS. 6+ live products run on a serverless platform I designed and reuse, and my recurring infra bill trends toward $0 by deliberate cost engineering: a documented S3-outbox relay pattern that replaces NAT gateways, free-tier tuning, and Durable Object hibernation. I am also an AI/LLM engineer in the strong sense: multimodal apps (vision + voice + RAG with pgvector), multi-provider orchestration (Anthropic, OpenAI, Gemini), LangGraph agents, LLM-as-judge evals, and an open-source agent framework I built on the Claude Agent SDK + Model Context Protocol: 20 agents, 47 skills, and 10 MCP tools that autonomously design, implement, and verify code behind human gates. Don’t trust the portfolio. Test me. Two ways: 1. AI briefing pack. Ask me for it, paste it into your own ChatGPT or Claude, and let YOUR AI evaluate my credentials. It is distilled from an automated audit of 26 of my real repositories and includes what I am still improving, because an honest document is more useful to you than a polished one. 2. Prompt challenge, free, one round. Send me a realistic prompt from your project: feature spec, bug report, or architecture question. I return the output of my AI-assisted workflow plus my human review: what I kept, what I corrected, and why. You judge the result and the judgment. Stack keywords: TypeScript, React 18/19, Next.js, Expo/React Native, tRPC, Drizzle, PostgreSQL, Supabase, AWS (Lambda, SAM, API Gateway, RDS, DynamoDB, CloudFront, Cognito), Cloudflare (Workers, Durable Objects, Pages), GitHub Actions OIDC, Clerk, LangGraph, Claude Agent SDK, MCP, RAG, Godot 4, n8n, Make, Zapier. Portfolio item blurbs Xenodot Forge (open source, MIT) = AI game-dev framework on the Claude Agent SDK + MCP: a human-gated design → implement → verify agent pipeline (20 agents / 47 skills / 10 MCP tools, ~14.7k LOC) driven from a WebSocket UI, with a verification-first quality gate (headless engine checks, zero-warning lint, CI-enforced validators). Used it to build a playable cross-platform 3D FPS in Godot 4.6. Available on my GitHub. Probius.app (live EdTech) = Study platform for the Brazilian OAB bar exam. React + Vite SPA on S3/CloudFront, Lambda + tRPC + Drizzle + PostgreSQL, NAT-less VPC posture with all outbound AI/email traffic via an S3-outbox relay, deployed exclusively through keyless GitHub Actions OIDC. Multimodal AI mobile app (~28k LOC) (3itch ai) = Fashion-critic app: Expo client, tRPC-on-Lambda backend, Cloudflare Durable Objects realtime engine. Vision (Gemini), persona brain (GPT-4o), voice in/out (Whisper + TTS), wardrobe RAG (pgvector), per-user AI spend caps, offline RS256 JWT verification on both backends, and gitleaks in CI. Production e-commerce (~31k LOC) = Luxury-furniture platform: Next.js 16 SSG storefront, React admin SPA, serverless API, and one PostgreSQL backend. Infra cost cut to ~$0 with the S3-outbox relay pattern, plus a single-query JSONB product search function that eliminates N+1 fetches. Multi-tenant PMS + OTA sync = Vacation-rental management system with Channex integration to Airbnb and Booking: idempotent booking sync, SQS worker queue, EventBridge polling, and a 12-scenario automated certification suite. Prompt Challenge I will run your prompt through my AI engineering workflow and hand you the reviewed output. Description: Send me one realistic prompt from your project: a feature spec, bug report, refactor, or architecture question. I run it through my AI-assisted workflow, using the same agent tooling I open-sourced, and deliver: (1) the raw output, (2) my line-by-line human review marking what I kept, what I corrected, and why, and (3) a short note on how I would productionize it. This shows you what you are actually hiring in 2026: not “can he prompt an AI,” but “can he judge, correct, and ship what the AI produces.” First round free on invite.

I'm Penetration Tester & Cybersecurity Consultant with 8 Years of Experience. I have been recognized as a Top Rated Plus freelancer on this platform🥇Take a look at my full profile to discover how I've helped clients secure their products and meet compliance goals. If you're looking to identify vulnerabilities before attackers do, strengthen your security posture, or meet compliance requirements, you're in the right place. Here's how I help businesses stay secure: 🛡️ Penetration Testing. End-to-end security testing for Web applications, APIs, Mobile apps, and Infrastructure. You'll receive a comprehensive report with not just a list of findings, but clear remediation guidance your team can actually use. 🛡️ Cloud Security & Compliance Readiness. I review and harden your cloud infrastructure to help you confidently meet industry standards including ISO 27001, SOC 2, PCI DSS, HIPAA, and more, without the guesswork. 🛡️ Microsoft 365 / Google Workspace Security. A holistic assessment and hardening of your Microsoft 365 or Google Workspace environment, covering identity, access controls, email security, and data sharing settings, so your team can collaborate confidently without exposing common misconfigurations that put your data at risk.

Full Stack Developer & BI Specialist | 5+ Years Building Data-Driven Solutions I transform complex business challenges into scalable technical solutions, specializing in inventory management systems, ETL pipelines, and business intelligence dashboards. Recent Impact: - Built end-to-end inventory management system for CANNPRISMA using React, Node.js, Python, and SQL Server - Designed ETL pipelines processing 1M+ records with Power BI dashboards driving business decisions - Migrated banking infrastructure from GCP to AWS, improving system reliability Core Expertise: Full-Stack Development | ETL & Data Engineering | BI Reporting | Cloud Architecture (AWS/GCP) | ERP Integration Tech Stack: JavaScript, TypeScript, React, NextJS, Node.js, Python, SQL Server, Power BI, Docker, AWS

I'm a senior AI engineer with a decade of experience across applied AI, software engineering, and tech consulting. I work with private equity firms, venture capital funds, family offices, and asset managers — both on the AI systems that automate their internal operations and as a forward-deployed engineer inside their portfolio companies. I spent four years inside a €600M B2B software fund, where I was the lead AI engineer responsible for shipping production AI systems that automated core fund workflows. Before that, I led digital transformation projects for large commercial banks at Deloitte Digital and KPMG. 𝐖𝐇𝐀𝐓 𝐈 𝐁𝐔𝐈𝐋𝐃 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐀𝐈 𝐟𝐨𝐫 𝐟𝐮𝐧𝐝𝐬 𝐚𝐧𝐝 𝐚𝐬𝐬𝐞𝐭 𝐦𝐚𝐧𝐚𝐠𝐞𝐫𝐬. Unified deal intelligence over fragmented sources (decks, websites, CRM, email, transcripts), automated deal sourcing with thesis-aligned filtering, CRM action layers via MCP, and standardized investment memos generated from the corpus. These are the systems I developed and ran for 4 years inside an active investment team. 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐈 𝐟𝐨𝐫 𝐩𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬. The bottlenecks I worked on inside funds show up almost identically inside portfolio companies — most often in logistics, manufacturing, procurement, and adjacent industries. The operational drag is the same: document-heavy processes where structured outputs turn unstructured text into useful data, and fragmented internal knowledge that needs RAG and function calling to be usable from inside chat or ERP-style interfaces. I've delivered this work for regulated clients in financial services, so role-based access control, PII redaction, audit trails, and tenant isolation are baked into how I build. 𝐒𝐄𝐑𝐕𝐈𝐂𝐄𝐒 I deliver AI projects end-to-end, without hand-offs between strategy and implementation. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲. Identify the AI opportunities that actually move a business metric. The job is to find the 20% that drives 80% of the outcomes. • 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧. Architect and ship the system. Production-grade, instrumented, evaluated, and integrated with existing infrastructure. Open-source-first to avoid vendor lock-in. Deployable on-prem or in the cloud depending on security and infrastructure constraints. • 𝐄𝐧𝐚𝐛𝐥𝐞𝐦𝐞𝐧𝐭. Transfer capability to the in-house team so they can operate, extend, and own what we built. 𝐌𝐘 𝐋𝐄𝐍𝐒 𝐎𝐍 𝐀𝐈 • 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐨𝐯𝐞𝐫 𝐭𝐨𝐨𝐥𝐬. AI engineering is still 80% systems engineering. The model is one of many moving pieces and rarely the right one to change first. • 𝐏𝐫𝐨𝐦𝐩𝐭 𝐚𝐧𝐝 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐛𝐞𝐟𝐨𝐫𝐞 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠. Most teams reach for fine-tuning too early. The leverage almost always sits earlier. • 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐨𝐯𝐞𝐫 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬. When the system is failing, the fix is usually in the architecture, not in the components. Better pipeline architecture beats swapping models or databases. • 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧-𝐜𝐞𝐧𝐭𝐫𝐢𝐜 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭. Evals are how you turn a non-deterministic system into a reliable one. Without them, every prompt change is a coin flip and every regression goes undetected. • 𝐒𝐤𝐞𝐩𝐭𝐢𝐜𝐚𝐥 𝐨𝐟 𝐟𝐮𝐥𝐥𝐲 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. A deterministic pipeline with LLM calls at specific steps beats a fully agentic system for most production problems. Map the process before adopting agentic flows. 𝐓𝐄𝐂𝐇 𝐒𝐓𝐀𝐂𝐊 • 𝐋𝐋𝐌 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: LangChain, LangGraph, CrewAI, Pydantic AI, LangFuse, LiteLLM, OpenAI and Anthropic SDKs. • 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐨𝐮𝐭𝐩𝐮𝐭𝐬 𝐚𝐧𝐝 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: Pydantic schemas as the contract between every LLM call and the rest of the system. • 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐚𝐧𝐝 𝐯𝐞𝐜𝐭𝐨𝐫 𝐬𝐭𝐨𝐫𝐞𝐬: PostgreSQL with pgvector by default. Qdrant, Weaviate, Pinecone when the engagement warrants a dedicated store. • 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: PostgreSQL, MySQL, Supabase. Alembic for migrations, SQLAlchemy when an ORM is needed. • 𝐀𝐏𝐈 𝐚𝐧𝐝 𝐚𝐬𝐲𝐧𝐜: FastAPI, Flask, Celery. • 𝐃𝐒 / 𝐌𝐋 / 𝐃𝐋: Pandas, NumPy, scikit-learn, XGBoost, TensorFlow, MLflow, Matplotlib. • 𝐎𝐩𝐬 𝐚𝐧𝐝 𝐜𝐥𝐨𝐮𝐝: Git, GitHub, Docker, uv for Python packaging, Streamlit for internal UIs. GCP, AWS, Azure, DigitalOcean, Heroku — I follow client preference. • 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬: Slack, WhatsApp, Telegram, Facebook Messenger, Instagram for conversational front-ends. MCP for tool-use and CRM action layers.