I design and deliver cloud and AI systems that turn complex requirements into reliable, production-ready platforms.
15+ years in tech, focused on Azure architecture, platform engineering, and intelligent systems. My current work centers on applied AI — small language model fine-tuning, running models at the edge, and designing multi-agent systems for real-world enterprise scenarios.
I embed with domain and engineering teams to turn ambiguous operational problems into production systems, then convert what works into reusable platforms.
Hands-on technical lead across discovery, architecture, implementation, evaluation, and production rollout for AI, data, and cloud systems in complex customer environments.
Architected and built an agent-driven system that turns previously unseen machine logs into production-ready OpenTelemetry metrics and events, links them to digital-twin components, and routes approved signals into the operational data platform.
Multiple collaborating agents form an end-to-end conversational loop that decomposes raw log templates and extracts the operational information hidden inside them. They share context, ground interpretations in domain knowledge, validate facts, challenge intermediate results, and revise outputs until deterministic quality checks are met.
Built the surrounding Python services, knowledge retrieval, vector search, messaging workflows, persistence, deployment infrastructure, and review experience required to operate the system in production while retaining human review for ambiguous results.
Python · Microsoft Agent Framework · Azure AI Foundry · Azure AI Search · Service Bus · Container Apps · SQL · Azure Data Explorer · OpenTelemetry
Owned the data and application path for an AI assistant that explains autonomous-driving behaviour from real vehicle context. Fine-tuned the on-device intent-recognition model and built the orchestration and backend services that route driver requests between grounded driving explanations, vehicle knowledge, and command APIs.
Developed in-car Python libraries that transform raw sensor, perception, and vehicle-state data into model-specific representations. Designed and implemented the end-to-end ingestion pipeline that captures event context, transfers it from the vehicle, stages it for low-latency retrieval, and supplies validated, model-ready inputs to the inference service.
Python · SLM fine-tuning · Constrained intent routing · Sensor-data engineering · Protocol Buffers · Azure Cache for Redis · Container Apps · Cosmos DB
Built a retrieval-augmented system for analyzing and comparing complex contracts. Designed hybrid-search indexes and ingestion pipelines, automated document extraction and metadata enrichment, and iterated prompt flows around real user question patterns.
Azure OpenAI · AI Search · Document Intelligence · Prompt Flow · Python
Helped establish a company-wide standard for core customer data. Built APIs and serverless applications, test and performance tooling, and the landing-zone infrastructure for reliable multi-stage deployments without production disruption.
C# · ASP.NET · Azure Functions · AKS · SQL · Bicep · Azure Landing Zones
Led the development workstream for global rollout of an image-recognition system at self-checkout terminals. Productized the edge deployment, established CI/CD, and enabled independently versioned releases per store.
Azure edge devices · AKS · Helm · Container Registry · Private networking
Engineered a reusable Azure blueprint for project-based data products. Extended the infrastructure codebase and built Python APIs that gave teams a consistent way to request and deploy services while aligning delivery across the wider program.
Python · Event Hubs · Data Lake · Azure Functions · Pulumi · Bicep · Azure DevOps
Focus: Azure adoption, modern device management, and long-term customer engineering support.
Responsibilities
Impact: Improved operational maturity for managed workplace platforms and enabled stronger cross-customer knowledge sharing through a regional expert community.
Focus: Configuration Manager expertise, enterprise support delivery, and technical enablement.
Responsibilities
Impact: Increased customer platform stability and confidence through hands-on engineering support and structured consulting engagements.
Focus: End-user service strategy, global workplace rollout automation, and client infrastructure operations.
Responsibilities
Impact: Helped scale and standardize workplace services globally, improving rollout consistency, operational control, and service quality.
