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I was building predictive systems long before GenAI became a job category. Today, I combine 15+ years of data science and analytics leadership with Machine Learning, Generative AI, LLMs, AI Agents, and modern data platforms to turn complex data into forecasts, insights, automation, and production-ready AI solutions. My experience spans legal, healthcare, marketing, technology, media, e-commerce, and entertainment. I have built forecasting models, customer intelligence systems, experimentation frameworks, data pipelines, data warehouses, and executive analytics used to support growth, revenue planning, retention, and strategic decision-making. More recently, I have focused on LLMs, RAG, Agentic AI, and private AI systems, including local LLM deployments and document intelligence for sensitive legal and healthcare workflows. SELECTED EXPERIENCE • Built statistical forecasting models for revenue planning, business growth, and executive decision-making • Built data warehouses and managed end-to-end ETL processes across multiple data pipelines • Served as Director of Analytics for 7+ years, applying Machine Learning, customer modeling, CRM analytics, experimentation, and statistical methods to business growth • Built predictive churn and customer behavior models to improve retention and membership strategy • Developed forecasting models for international web traffic, search trends, advertising inventory, and product planning • Applied regression, Random Forest, K-means clustering, segmentation, fuzzy matching, and A/B testing to real business problems • Led analytics supporting nearly 60 media properties and trained more than 1,000 users on analytics systems • Built private LLM solutions using Llama and Gemma with RAG-based document indexing and GPU-tuned inference • Developed AI use cases for legal and healthcare workflows, including deposition analysis, contract risk review, SOAP-note generation, and prior-authorization drafting WHAT I CAN HELP YOU BUILD • Predictive models for revenue, demand, churn, customer behavior, and growth • Machine Learning solutions for classification, regression, clustering, segmentation, and forecasting • GenAI and LLM applications connected to business data, APIs, databases, and workflows • RAG systems for enterprise search, document intelligence, and internal knowledge assistants • AI Agents and multi-agent workflows using LangChain and LangGraph • Private and local AI solutions using open-source LLMs • AI-powered analytics and executive decision-support systems • ETL and ELT pipelines, data integration, and analytics automation • Modern data platforms using Databricks, Snowflake, Spark, and PySpark • Customer segmentation, LTV, CRM, funnel, and marketing analytics • A/B testing, experimentation, statistical analysis, and predictive modeling AI, ML & DATA STACK Generative AI & LLMs: LLMs, RAG, LangChain, LangGraph, AI Agents, Multi-Agent Systems, Hugging Face, Llama, Gemma, embeddings, vector search, prompt engineering, document intelligence, local and private LLM deployment Machine Learning & Model Development: Python, PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, supervised and unsupervised learning, regression, classification, Random Forest, K-means clustering, feature engineering, model evaluation, hyperparameter tuning, predictive modeling, forecasting Data Engineering & Platforms: SQL, Databricks, Snowflake, Spark, PySpark, ETL/ELT, data pipelines, data warehousing, data integration, dbt, Airflow, MLflow Analytics & Statistics: Predictive Analytics, Statistical Modeling, A/B Testing, experimentation, customer segmentation, churn modeling, LTV, CRM analytics, marketing analytics, behavioral analytics, revenue forecasting, R WHY WORK WITH ME I understand how to connect data, models, AI, infrastructure, and business objectives into practical systems that support real decisions. My background combines 15+ years of commercial data science and analytics experience, Director-level leadership, Machine Learning and predictive modeling, GenAI and LLM development, modern data platforms, and scientific research. I also have postgraduate training in Artificial Intelligence and Machine Learning, an MS in Mechanical Engineering, an MA in Psychology, and a BS in Mechanical Engineering with Honors. My scientific research includes published work in measurement, modeling, fluid mechanics, and engineering systems. I am particularly effective when the problem is complex or not fully defined. If you have business data, documents, analytical workflows, models, or an AI initiative, I can help structure the problem, identify the right technical approach, and turn it into a working solution. Send me a short description of what you are trying to solve, the data or systems you currently have, and what a successful outcome looks like.

I build production data systems that run correctly after I leave — pipelines, LLM workflows, backtesting engines, and Excel/VBA automation. Not prototypes. Not one-off scripts. Former Amazon Data Engineer. Doctor of Engineering, Data Engineering, University of Florida. Currently delivering active production systems for a US-based quantitative research firm. WHAT I'VE DELIVERED IN PRODUCTION: → Claude API & LLM workflows — document automation and agent pipelines using the Anthropic API, integrated into existing data systems. → Weekly Tech Tracker — config-driven Python pipeline processing 280,930 rows across 66 companies every week. One config change adds a new grouping. No code deploy required. → Valuation Anchor Engine — 817-day backtesting system across AMZN, GOOGL, MSFT, ORCL. HAC/Newey-West corrected, FDR-adjusted. 100% in-sample hit rate on best-anchor signals — reported honestly as in-sample, not a forward guarantee. → Macro Data Pipeline — 20+ FRED series, zero manual refresh steps, live Looker Studio dashboard. Client scaled the engagement from 30 to 40 hours/week after the first delivery. → JST Service Awards — VBA Excel automation across 14 business units, 1,000+ employees. Full refresh under 60 seconds, zero formula errors, CHRO-approved. → National ML Infrastructure — Random Forest/XGBoost ensemble (0.89 AUC-ROC), deployed across 3 national projects, adopted by an international development partner as their primary screening tool. 47 regions, 160+ staff trained. WHERE I FIT BEST: - Claude & LLM integration — API workflows, document automation, RAG pipelines, agent workflows - Data engineering & ETL — Python, SQL, Snowflake, BigQuery, dbt, config-driven pipelines - Quant finance — backtesting, statistical validation (HAC/NW, FDR), financial modeling - Excel/VBA & BI automation — Power BI, Looker Studio, Tableau WHAT SEPARATES MY WORK: Config-driven by design — output updates when an input changes, no developer required after handover. QA runs automatically on every execution, not as an afterthought. I separate in-sample results from forward-tested ones and say so plainly, before a client finds out the hard way. That discipline is why the quant research firm I currently work with grew my engagement rather than shopping around. Python · SQL · Anthropic Claude API · LLM Prompt Engineering · Snowflake · BigQuery · dbt · Google Apps Script · Looker Studio · Power BI · pandas/NumPy/scipy · statsmodels · scikit-learn · XGBoost · VBA Excel Doctor of Engineering, Data Engineering, University of Florida. Previously Amazon (data engineering, ETL, Power BI). Trilingual: English, Swahili, Spanish. Send me what you're working with — I'll give you a straight answer on fit, timeline, and cost before you spend a single connect.

Demonstrated experience driving impact with Analytics, ML, GenAI, and Automations across startups and Fortune 100 and smaller companies - in Healthcare, Retail, Insurance, Marketing, and Fintech. AI Engineer | Data Science & GenAI | MVPs, SaaS & Production AI | Healtcare/Fintech I help startups and growing businesses turn data and AI into practical, revenue-impacting products. With 6+ years of hands-on experience in AI, machine learning, and full-stack AI systems, I focus on building solutions that actually ship, scale, and deliver value, not just experiments. My background spans AI-powered MVPs, SaaS platforms, automation systems, and data-driven products, where AI is tightly integrated into real business workflows. I work closely with founders and product teams to move from idea to prototype and into production with clarity and speed. What I Can Help You Build AI & Generative AI: LLM-based applications (chatbots, copilots, internal tools) RAG systems for documents, knowledge bases, and search Prompt engineering, fine-tuning, and AI workflow design AI agents for operations, support, sales, and analytics End-to-End Data Science: Data cleaning, feature engineering, and modeling Predictive models (forecasting, churn, risk, recommendations) NLP solutions (classification, summarization, semantic search) Model evaluation and performance optimization Production & Deployment: APIs for AI systems (FastAPI, Node.js) Scalable deployments using Docker and cloud platforms Secure integrations with existing products and databases Monitoring, logging, and iteration-ready architectures Product & MVP Focus: AI-first MVPs for startups SaaS features powered by ML/LLMs Automation to reduce manual effort and operational cost Clear technical roadmaps aligned with business goals Tech Stack Data Science: Pandas, Scikit-Learn, Keras, PyTorch, MLFlow, Plotnine, Tableau Gen AI & Agentic AI: Large Language Models, MCP, LangChain, Multi-agent systems, CrewAI, Retrieval Augmented Generation, Pinecone Automation: Zapier, n8n, make ML & AI: Feature Engineering, Classification, Regression, Hyper-parameter Tuning Accelerated Machine Learning: GPU-boosted RAPIDS, cuDF, cuML, XGBoost; Spark, Databricks, DASK Programming Languages: Python, SQL, Scala, C/C++, Java, JavaScript, HTML How I Work: I think product-first, not model-first I communicate clearly with both technical and non-technical stakeholders I design systems that are maintainable, scalable, and explainable I’m honest about scope, trade-offs, and timelines Why Me Strong balance of AI depth + software engineering Experience shipping real products, not just notebooks Business-aware decisions (performance, cost, usability) Reliable communication and milestone-driven delivery If you’re looking to build an AI-powered MVP, SaaS feature, or automation system that actually works in production, I’d be happy to discuss your idea and suggest a clear, realistic path forward. Tableau Dashboards, Business Intelligence, Open to taking on new projects. Drop me a message!

I am a Ph.D. engineer and quantitative analyst with over 10 years of experience in mathematical modeling and time-series analysis. My expertise lies in developing advanced analytical methods that are applicable in both scientific and financial contexts. I specialize in designing adaptive Kalman filters, time series modeling, implementing particle swarm optimization, and performing regression modeling to extract actionable insights from complex datasets. My ability to simplify technical findings ensures clear communication with diverse stakeholders. Whether you need to enhance predictive modeling for financial markets or implement sophisticated analytics in research, I bring a unique blend of technical proficiency and practical experience to help you achieve meaningful results. Let's discuss how I can support your project and drive impactful outcomes.

Principal AI Engineer | GenAI, Edge AI, RAG & Agentic Workflows I build production-ready AI solutions, not just prototypes and demos. I am Matthew - a Principal AI Engineer and Data Scientist with over 20 years of experience solving complex enterprise technology and data problems. I specialize in Generative AI, Agentic workflows, machine learning, data engineering, and anticipating the next frontier of intelligent automation. Currently serving as a Principal AI Engineer at a Fortune 50 enterprise and holding an M.S. in Data Science from Northwestern University, I bring enterprise-grade architecture and rigor to businesses of all sizes. I don't just connect applications to an API; I understand the entire AI lifecycle. Furthermore, I architect future-proof systems—leveraging emerging paradigms like Edge AI, Small Language Models (SLMs), and Multi-Agent Swarms to ensure your tech stack is ready for the demands of 2027, 2030, and beyond. Here is how I can help you build something that actually works: 🔹 EDGE AI & NEXT-GEN ARCHITECTURE (2027+ Readiness) Edge AI & TinyML: Deploying lightweight, high-performance ML and AI models directly to IoT and edge devices for zero-latency, offline, and privacy-first capabilities. Small Language Models (SLMs) & Local AI: Fine-tuning and deploying highly efficient, domain-specific models that drastically cut cloud compute costs and keep enterprise data secure on-premise. Federated Learning: Architecting decentralized model training across distributed networks to maximize data privacy. Multi-Modal AI: Seamlessly integrating real-time vision, audio, and spatial data streams for advanced physical and ambient AI applications. 🔹 GENERATIVE AI & LLM APPLICATIONS Custom GenAI applications & Enterprise LLM/SLM solution architecture Prompt engineering, optimization, and structured outputs (tool calling) AI-powered document and intelligent workflow automation LLM evaluation, testing, guardrails, and optimization 🔹 AI AGENTS & AGENTIC WORKFLOWS Multi-agent swarms and complex autonomous AI orchestration LangGraph workflows & Tool-enabled agents Human-in-the-loop workflows & Model Context Protocol (MCP) Agent evaluation and enterprise production readiness 🔹 RAG & ENTERPRISE SEARCH Retrieval-Augmented Generation (RAG) architecture Embeddings, vector databases, and semantic search Knowledge-base assistants and document ingestion pipelines Retrieval accuracy and groundedness evaluation 🔹 MACHINE LEARNING & DATA ENGINEERING Predictive modeling, classification, segmentation, and anomaly detection Forecasting, time-series analysis, and recommendation systems Large-scale data processing (PySpark, Apache Spark, Databricks, Snowflake) MLOps architecture, continuous learning, and model validation 🏆 MY BACKGROUND & CREDENTIALS: Experience: 20+ years in enterprise tech, data, analytics, ML, and AI. Current Role: Principal AI Engineer / Data Scientist at a Fortune 50 enterprise. Education: M.S. in Data Science, Northwestern University. Innovation: U.S. Patent Inventor. Tech Stack: Azure, Databricks, Snowflake, Spark, Python, SQL, LangChain/LangGraph, Edge AI Frameworks, and modern AI/ML platforms. I am equally comfortable designing forward-looking AI architecture, building complex agentic workflows hands-on with Python, or translating deep technical concepts into clear business value for executives and stakeholders. Whether you need a cutting-edge Edge AI deployment, a robust RAG solution, or help taking an AI concept from a fragile idea to a secure, scalable production deployment, I am here to help. Have an AI, ML, or data challenge? Hit the "Invite" or "Hire" button, send me a message, and let's discuss what you’re building.

Looking for someone who gets the job done? I'd love to help. What I do: UGC Content, Product Modeling, Video Editing, AI Training & Data Entry & Labeling, Product, App & Website Testing, Voice Recording & Graphic Design yes, jack of all trades lol, I focus on delivering quality work, communicating clearly, and making the process easy from start to finish. I'd love the chance to work with you!