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I act as a Digital-Physical Systems Architect(Engineer), bridging the high-resolution gap between computational simulation and 3-micron physical execution. My workflow is governed by a Zero-Error Philosophy: I do not provide "attempts"; I engineer solved outcomes. Strategic References Rolls-Royce Marine: Manufacturing Lead for 5-meter propulsion assemblies and thrusters. Optimized high-speed kinematics and manual G-code for heavy industrial maritime systems. • Maersk & Siemens: Tier 1 Liaison managing strategic governance, data traceability, and operational integration for global infrastructure. • Danish Government & PET: Handled sensitive infrastructure projects requiring absolute operational security and zero-error data frameworks. • Defense & MedTech: Specialized in thermal management for Defense HVAC and ISO 13485 compliant manufacturing for the Japanese medical market (3-micron tolerances). The Integrated Execution Loop For Tier 1 government, defense, and MedTech (ISO 13485, SMACNA) projects, failure is a result of fragmented data. I eliminate this friction by integrating the entire loop: • Virtual Validation (CFD & FEA): High-fidelity simulation of fluid-to-air heat transfer (STAR-CCM+, nanoFluidX) and structural integrity (Simcenter Nastran). I validate the physics before a single resource is committed. • Design for Manufacturing (DFM): Translating complex geometry into flawless CAD/CAM architecture (NX, SolidWorks or any preferred file format). I ensure that the 3D model, technical drawings, and G-code are a perfectly synchronized loop. • High-Precision Production: Expert-level mastery of 5-Axis Milling and Swiss-Type Turning (Citizen/Nakamura-Tome, Fanuc/robotics) for exotic materials like Titanium and PEEK. Strategic Governance & Value My background includes designing data traceability frameworks for PET, the Danish Government, and leading manufacturing for Rolls-Royce Marine (5-meter propulsion assemblies). I specialize in Operational Turnaround, using first-principles logic to solve engineering friction that traditional vendors miss. The Zero-Error Guarantee I do not accept payment for unstable or unverified solutions. I prioritize Fixed-Price, Solution-Based contracts because my value is measured by the elimination of technical risk. Note on Availability: To maintain 100% fidelity and precision tolerances, I accept only a limited number of high-stakes strategic partnerships per year.

I build and evaluate production AI agents for real business workflows. I work from ambiguous requirements through architecture, implementation, evaluation, deployment, observability, and handoff. Recent delivery: I completed Kinetix Mentor through a $5,600 / 140-hour Upwork engagement. It is a deployed WhatsApp AI platform combining Gemini models, LangGraph orchestration, Pinecone RAG, PostgreSQL memory, multimodal processing with GCS, Stripe subscriptions, external research tools, LangSmith tracing, FastAPI, and Docker. The client gave the project 5 stars and described me as “a complete professional who gets the job done.” I am now turning Kinetix into a measurable Agent Evaluation system. Current work includes: • Versioned JSONL evaluation datasets and repeatable model runners • Deterministic scoring for tool selection, routing, arguments, security, and response contracts • Repeated live-model evaluation instead of one-off testing • Trace-driven failure analysis and targeted regression testing • Separation of scorer defects, agent defects, and infrastructure failures The first router baseline evaluated 30 repeated model runs, uncovered both measurement defects and genuine agent failures, and produced a 17/17 passing targeted regression after fixes. I can help you: • Build or repair LangGraph, RAG, and tool-using AI agents • Create evaluation datasets, behavioral scorers, and regression suites • Diagnose production traces and improve reliability, quality, latency, and cost • Integrate FastAPI, PostgreSQL, vector databases, webhooks, payments, and external APIs • Turn an AI prototype into a deployed, observable workflow with clear documentation and handoff Before freelancing, I worked in ByteDance’s international business, building data-driven systems and translating ambiguous business problems into measurable technical outcomes. Best fit: AI Agent and RAG systems, LLM evaluation, existing AI product improvements, reliability audits, and production integrations. Send me your workflow, current architecture, failure examples, and success criteria. I’ll propose a concrete first milestone.

Hi, I'm Fran 👋 I architect and ship production AI systems and cloud infrastructure that actually scale. → Architected & shipped a production AI copilot (agentic, RAG-grounded, human-in-the-loop) now serving customers → Sr DevOps running cloud infra for a NASDAQ-listed biotech, supporting Twist Bioscience (NASDAQ: TWST, $2B+) → Cut report generation time 50% at IBM ($150B+ market cap) with Python microservices → Cut cloud infrastructure costs 30% for a US biotech SaaS company using GCP rightsizing + autoscaling → 2× release velocity at a US biotech SaaS by streamlining CI/CD → Built recurring AI consulting from $0 to $1,500+/client serving LATAM tech professionals 8+ years building production systems that move real money 24/7. CKA + CKAD certified (Linux Foundation / Cloud Native Computing Foundation). 💼 What I do: → Cloud architecture (AWS, GCP, Azure) — Kubernetes, Terraform, ArgoCD → AI Agents, RAG & Automation — Claude / OpenAI, Python, production-grade → Infrastructure cost optimization — typical 25-40% savings → CI/CD pipeline acceleration — typical 3-5× speedup → Production systems on your existing stack (no rip-and-replace) 🎯 Best fit for: → B2B SaaS with infrastructure scaling challenges → Legal/professional firms needing AI document automation → Marketing agencies needing content automation systems → Teams needing senior engineering on fractional/project basis Stack: Kubernetes · Terraform · ArgoCD · AWS · GCP · Azure · Python · Go · Claude Code · GitHub Actions Let's chat 👇

🔥 I turn raw data into decisions : 12+ years of analytics at GlaxoSmithKline and across the pharma industry. MSc Bioinformatics, Imperial College London. 🟢 My Superpower I'm your "data translator"; I speak Python, R, and SQL fluently, but I translate everything into insights your stakeholders will actually understand and act on. 💼 Who I Help Pharma companies, healthcare orgs, and business leaders tired of spreadsheets that tell them nothing and dashboards that confuse everyone. 😏 Why Clients Hire Me - 12+ years across 3 countries (UK, France, Morocco) - Dashboards at GSK that increased decision-making speed by 20% - Automated reporting that saved teams 40% of their time - Statistical analysis that drove 15% regional sales growth 🤯 What I Do - Build Power BI & Tableau dashboards your team will actually use - Design surveys that produce actionable insights, not just charts - Automate reporting with SQL & Python so you focus on strategy - Rigorous statistical analysis (SPSS, JMP, SAS, R) 🧩 Core Skills ✔ Power BI, Tableau, R Shiny, MicroStrategy ✔ Python, R, SQL, SAS ✔ SPSS, JMP, Minitab, Statistical Modeling ✔ English (Fluent) | French (Native) | Arabic (Native) ✅ MSc Bioinformatics : Imperial College London | BSc Mathematics University of Leicester 👩‍💻 If you want someone who turns data chaos into clear decisions, let's talk.

AI Solutions Architect with 10+ years designing enterprise decision systems and shipping production AI agents. Founder of Acumis, an AI consultancy and SaaS company. Selected speaker at SAS Innovate 2026 on enterprise decisioning architecture and modernization. WHAT I BUILD FOR CLIENTS • Voice AI agents, like DentalDesk, my production voice-AI receptionist for dental practices that handles 24/7 inbound calls, books appointments in real time, and escalates urgent cases end-to-end. • LLM applications & RAG platforms (FastAPI, PostgreSQL with pgvector, Ollama, OpenAI/Anthropic APIs), I designed and led development of Jasper, an internal AI analytics platform delivering natural-language analytics to non-technical business users over heterogeneous enterprise data. • Enterprise AI architectur, I bring 10+ years of enterprise production discipline (SAS Viya, decisioning systems, data pipelines across AS400/DB2/PostgreSQL) to AI projects. I presented my architecture work at SAS Innovate 2026 on migrating a specialty auto finance company's core credit decisioning to SAS Intelligent Decisioning. • Prediction modeling & classical ML, xgboost and scikit-learn models for risk analysis and multi-objective decisioning problems. HOW I WORK I work end-to-end: problem definition, architecture, build, deployment, and operational tooling. Not just specs handed off, not just implementation waiting for direction. I own the quality bar from start to finish. My design principles: prompt design as governed artifacts, structured output generation, tool-use where it beats direct generation, human-in-the-loop where the cost of being wrong is high, and observability from day one. OPEN TO • AI Solutions Architecture engagements • Voice AI builds (Retell, Twilio, telephony integration) • LLM and RAG platform design • Agentic automation and multi-step workflow design • Enterprise AI advisory • Prediction modeling and multi-objective optimization projects Python (xgboost, scikit-learn, pandas, numpy, FastAPI) | Node.js / TypeScript / Next.js | PostgreSQL with pgvector | Retell, Twilio, Cal | Ollama, OpenAI APIs, Anthropic Claude | Docker, GitHub Actions (CI/CD), Railway, Vercel | SAS Viya, SAS Intelligent Decisioning | SQL across PostgreSQL, DB2, AS400 environments

I turn tangled data and hard modelling problems into things you can actually use — a working model, a deployed app, a clear answer to a question that mattered enough to pay someone to get right. I hold a PhD in Complexity Sciences and a first in Theoretical Physics, and I've spent ~15 years as a researcher and consultant building this stuff for real. Most of that work has been across fields that don't usually talk to each other: critical-care medicine, theoretical ecology, economics, education, and industry. That range isn't a gimmick — the same handful of methods (machine learning, agent-based simulation, network analysis, optimisation) keeps showing up in different disguises, and having applied them in genuinely different settings means I recognise which one your problem actually needs, rather than reaching for whatever's fashionable this quarter. A few concrete things, so this isn't just adjectives: I built and deployed a Flask decision-support app for ICU discharge that came out of a machine-learning study (published in BMJ Open). I've written agent-based models of illegal fishing and GPU-accelerated simulations with large speed-ups over the naive version. I've done reinforcement-learning and optimisation for allocating people at organisational scale, control software for off-grid sanitation hardware, and Streamlit data tools for public-good projects. My research has appeared as first-author work in Nature Communications, PNAS, and BMJ Open. Two things I'm reliably good at: picking up an unfamiliar technique and getting it working quickly, and explaining a complicated result to the people who have to act on it without dumbing it down. I work in full-stack Python and am comfortable across the whole pipeline — extraction and cleaning, exploratory analysis, model building, and getting it deployed somewhere people can click it. I run a small consultancy, Rusty Data, focused on helping organisations get value out of data they're already sitting on. If you've got a modelling problem, a pile of data you suspect is useful, or an idea you want pressure-tested by someone who'll tell you honestly whether it'll work — send me a message with a bit of detail and I'll tell you how I'd approach it. How I can help: Build and deploy ML models — classifiers, deep learning (CNN/RNN), predictive tools — end to end, not just a notebook. LLM and agentic work: RAG systems, NLP pipelines, and automation of the tedious analysis you'd rather not do by hand. Simulation and modelling: agent-based models, dynamical systems, and network analysis, including GPU-accelerated versions when speed matters. Optimisation and decision support: numerical optimisation, reinforcement learning, and bespoke apps that turn a model into something a team can use. Data wrangling and exploratory analysis on messy, large, or HPC-scale datasets. Data visualisation and interactive dashboards (d3.js, Tableau, Streamlit, Bokeh) that make a result legible. A second opinion — feasibility reviews, method selection, and clear write-ups for technical or non-technical stakeholders. Skills & tools: Languages: Python (full-stack), SQL (MySQL/Postgres), C++, R, MATLAB/Octave, NetLogo, Unix/shell Python stack: TensorFlow, Keras, PyTorch, scikit-learn, Pandas, SciPy, NumPy, Matplotlib, Bokeh, Streamlit, Flask, Django, Jupyter Infrastructure: big data, HPC, GPU acceleration, cloud compute Visualisation: d3.js, Tableau, Streamlit, Bokeh, Matplotlib Methods: ML (classifiers, CNN/RNN deep learning, deployment) · LLMs (NLP, RAG, agentic workflows, automation) · reinforcement learning · agent-based & dynamical-systems modelling · network analysis & inference · dimensionality reduction (PCA, t-SNE, kernel) · structural equation models & causal inference · numerical optimisation (simulated annealing, basin hopping) · full-stack software development · data visualisation