Operationalizing AI Agents with Microsoft Foundry and Azure Services
A practical look at moving agent-based AI solutions from proof of concept to resilient production deployment.
Read moreI help organizations design and deliver enterprise analytics platforms, AI applications, and agent-based solutions on Azure.
Hands-on expertise across modern data warehousing, analytics engineering, machine learning, and enterprise AI solutions.
Design end-to-end data platforms across Azure and Snowflake using scalable patterns for ingestion, transformation, governance, and analytics.
Design enterprise AI applications with Azure AI services, and Microsoft Foundry-based agents that can move into production.
I work across solution design and execution, from warehouse modeling and pipelines to machine learning deployment and AI agents.
Experience spanning Microsoft, EY, consulting engagements, research, and industry operations gives me a pragmatic view of what actually works.
My approach is grounded in dimensional modeling, ETL design, SQL, Python, PySpark, and the realities of building reliable platforms.
I have led engineers, built proof of concepts, mentored teams, and helped clients adopt delivery practices that outlast a single project.
Examples grounded in the kinds of architecture and implementation work I have delivered across industries.
Challenge
Inbound logistics data was spread across SAP ECC, S/4, and freight-forwarder APIs, making it difficult to build a consistent analytical view.
Solution
Designed and implemented an end-to-end solution with ADLS, Azure Event Grid, Dagster, Snowflake, and dbt across landing, raw, and mart layers.
Challenge
Teams needed a production-ready way to classify newly added products from product descriptions while keeping security and monitoring in place.
Solution
Architected a machine learning solution using ADLS, Azure Databricks, Azure Machine Learning, Key Vault, Container Registry, AKS, VNets, and Azure Monitor.
Challenge
Operations and maintenance teams needed better visibility into performance, root causes, and maintenance activity across more than 100 initiatives.
Solution
Built KPI dashboards in Power BI, partnered with business leaders on analysis, and integrated maintenance management with inventory workflows.
Perspectives shaped by delivery work in data platforms, AI implementation, analytics engineering, and technical enablement.
A practical look at moving agent-based AI solutions from proof of concept to resilient production deployment.
Read morePatterns for building raw, business, and mart layers with maintainable orchestration and deployment standards.
Read moreWhy modeling discipline, orchestration design, and team enablement matter as much as tool selection.
Read moreLessons from building AI agents that integrate with enterprise data platforms and Azure services.
Practical patterns for dimensional modeling, Data Vault, dbt transformation layers, and maintainable orchestration.
How to move machine learning solutions from experimentation into monitored, containerized, enterprise-grade deployment.
"Architecture decisions are only valuable when they can be implemented, operated, and adopted by real teams."
Data platforms, AI, and analytics delivery
"Strong platforms come from disciplined modeling, dependable pipelines, and delivery practices that scale with the team."
Snowflake, Azure, dbt, and orchestration
"The goal is not just experimentation. It is getting reliable analytics and AI capabilities into production with clear business value."
AI solutions, MLOps, and platform modernization
Whether you need platform modernization, AI solution design, warehouse architecture, or delivery leadership, I'm open to a conversation.