Matthew S.

𝗜 𝗯𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗽𝗿𝗼𝘁𝗲𝗰𝘁 𝗯𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗶𝗻 𝗿𝗲𝘃𝗲𝗻𝘂𝗲. 𝗡𝗼𝘁 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲𝘀—𝘀𝗲𝗰𝘂𝗿𝗲, 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝘁𝗵𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. Most AI initiatives fail because they lack the engineering discipline to scale. I bridge the gap between Data Science and MLOps, ensuring your systems are not only "smart" but also secure, compliant, and cost-efficient. With over 8 years of experience across IBM, Deloitte, Booz Allen, and Accenture, I specialize in taking AI from "experimental demo" to "mission-critical reality." I led the development of the first open-source ML platforms for the IRS, building systems that govern models protecting over $5B in federal revenue. Big Tech & Enterprise Pedigree: - SAIC & Booz Allen: Lead engineer for high-scale AI platforms in the US’s most regulated environments. - Accenture & Deloitte: Led engineering teams in deploying ensemble fraud models and Medicare claim protection systems. - IBM: Developed first-generation ML models protecting $10M+ in revenue. High-Impact Solutions for Your Business: - Agentic AI & Voice Intelligence: I build multi-agent systems (LangGraph/AutoGen) that navigate complex business logic and execute API actions autonomously. I architect low-latency voice pipelines (Deepgram/ElevenLabs) for real-time AI interactions. - Traditional ML & Fraud Detection: I have a deep background in classical ML. I’ve deployed 30+ ensemble supervised models for fraud detection, using advanced feature engineering and XGBoost/LightGBM to identify patterns in petabyte-scale data. - Computer Vision & Document AI: I build CV pipelines for industrial and medical use cases—ranging from processing Medicare provider forms to image segmentation and object detection in high-security environments. - LLM Fine-Tuning & RAG: When off-the-shelf models aren't enough, I fine-tune LLMs (using LoRA/QLoRA) for specific domains. I build high-performance RAG systems with <1 second response times and zero-hallucination guardrails. - Databricks & AWS MLOps: I specialize in the "Modern Data Stack." I recently migrated 500+ data scientists to Databricks E2, reducing code runtime by 80% and slashing cloud costs by 30%. - Responsible AI & Governance: I build "Safety Rails" into the architecture—creating custom frameworks for drift monitoring, auditability, and bias elimination, critical for Fintech and Healthcare. Technical Stack: - AI/ML: LLMs (Claude, OpenAI, Llama 3), Fine-Tuning (PEFT, LoRA), RAG. - Computer Vision: OpenCV, TensorFlow, PyTorch, Image Segmentation. - Agentic Frameworks: LangChain, LangGraph, CrewAI, AutoGen. - Modern Data Stack: Databricks (Unity Catalog), AWS (CDK, SageMaker, Bedrock), Snowflake. - Engines: PySpark, Spark, Delta Lake, Kubernetes, Docker, MLflow. - Traditional ML: Scikit-learn, XGBoost, CatBoost, SAS, R. I take full ownership of the system architecture, documentation, and team enablement. If you need a partner who understands the rigors of enterprise security and the complexities of the full AI spectrum, let’s talk.