I am an AI content systems engineer, foundation model researcher, and Carnegie Mellon-trained data scientist. I specialize in designing large-scale multi-agent ecosystems, distributed orchestration pipelines, and the deep semantic layers required to transform continuous stream metrics into reliable, stateful enterprise applications.
My work focuses on solving complex structural bottlenecks in machine learning engineering: managing state persistence across long-running autonomous loops, optimizing context retrieval for time-series data, and establishing rigorous technical data governance for multi-node model architectures.
- Multi-Agent Systems: Developed and open-sourced an autonomous, multi-agent orchestration framework built to execute complex, long-running agentic workflows without losing contextual integrity.
- Memory Architecture: Engineered custom semantic layers, graph frameworks, and distributed vector database pipelines (RAG) to optimize long-context retrieval, manage asynchronous execution loops, and handle multi-agent task dependencies.
- Data Stream Orchestration: Built low-latency data pipelines designed to ingest, process, and structure complex time-series data and continuous physiological stream metrics.
- Technical Governance: Championed enterprise-level data integration strategies and observability pipelines that ensure data integrity, privacy, and low-latency throughput across high-performance compute environments.
- Cluster Compute: Leveraged advanced physical and cloud infrastructure, including high-density NVIDIA hardware clusters, to execute, benchmark, and scale high-throughput model pipelines.
- Evaluation Frameworks: Designed custom MLOps testbeds to evaluate tokenization efficiency, measure prompt performance under heavy context windows, and resolve scaling bottlenecks.
- Production Deployment: Spearheaded the technical architecture of AI-powered digital workers and advanced RAG systems from initial prototyping to hardened execution frameworks.
- Enterprise Validation: Partnered with leadership teams at Microsoft (Fabric and Copilot), AT&T, Verizon, and CVS Health to deploy scalable language interfaces built on measurable trust, performance, and alignment indicators.
- Languages: Python, TypeScript, SQL, Rust
- Agentic Frameworks: LangChain, LangGraph, LlamaIndex, DSPy, Semantic Kernel
- Foundation Models & APIs: OpenAI API, Claude, Llama, Azure OpenAI Service
- MLOps & Infrastructure: Azure AI Foundry, Azure AI Search, MLflow, Weights & Biases, Hugging Face
- Data & Vector Systems: Vector Databases (Pinecone, Qdrant, Milvus), Graph Databases, Distributed ETL
Based in Boston, Massachusetts. I am always open to discussing foundation models, time-series data engineering, and the architecture of autonomous agent ecosystems.
"The purpose of abstraction is not to be vague, but to create a new semantic level in which one can be absolutely precise." — Edsger W. Dijkstra