Expertise

Where the Work Happens

Five areas that tend to overlap in real engagements: architecture decisions, Microsoft Cloud & Security, the infrastructure underneath, the software that automates it, and the AI layer that's increasingly part of all four.

Solution Architecture & Consulting

Turning a request into a concrete plan, with the risk worked out before implementation starts.

Core capabilities

  • Requirements analysis & technical discovery
  • Solution design across high-level and low-level architecture
  • Cloud & hybrid architecture planning
  • Security architecture review
  • Migration & transition planning
  • Technical feasibility & risk assessment
  • Solution validation & operational handover

Technology ecosystem

Architecture Decision RecordsHLD / LLD diagramsRisk registersRunbooks

Engineering considerations

  • A wrong assumption made during discovery usually surfaces later, after the architecture is already built around it, when it costs more to fix.
  • Feasibility and risk get assessed while the architecture can still change, while there is still room to adjust.

Microsoft Cloud & Security

Identity, endpoint, and data protection across the Microsoft 365 and Azure ecosystem, with Zero Trust as the organizing principle.

Core capabilities

  • Identity & access management design
  • Conditional Access & MFA policy design
  • Hybrid identity: Active Directory + Entra ID
  • Endpoint management & compliance (Intune)
  • Information protection & DLP
  • Zero Trust architecture

Technology ecosystem

Microsoft 365Microsoft Entra IDActive DirectoryMicrosoft IntuneMicrosoft DefenderMicrosoft PurviewExchange OnlineSharePoint OnlineMicrosoft TeamsMicrosoft GraphPower Platform

Engineering considerations

  • Conditional Access changes get staged and tested against a break-glass account before a wide rollout, because a lockout causes more damage than a delayed rollout.
  • DLP and sensitivity labels only work if they match how people actually do their jobs day to day; policy documents alone don’t capture that.

Infrastructure & Networking

The network and server layer underneath everything else: segmentation, access control, and the Linux services that keep it running.

Core capabilities

  • Network architecture & segmentation
  • Switching, routing & VLAN design
  • Firewall policy & SD-WAN / VPN
  • Virtualization & hybrid infrastructure
  • Containerized deployment
  • Monitoring & operational troubleshooting

Technology ecosystem

Windows ServerLinuxFortinetCiscoUniFiDockerNginx

Engineering considerations

  • In practice, firewall exceptions are usually what breaks a segmentation plan, so every exception gets a documented reason and an expiry date.
  • Putting a service in a container doesn't reduce how well you still need to understand what it depends on underneath.

Software Engineering & Automation

Writing the code that turns a manual process, or a one-off script, into a tool other people can actually rely on.

Core capabilities

  • Web application development
  • REST API integration & authentication
  • Automation workflows & data processing
  • Backend services & database integration
  • Containerized deployment & observability

Technology ecosystem

PythonPowerShellC# / .NETREST APIsDocker

Engineering considerations

  • An automation script nobody but its author can read is a single point of failure, whatever label it gets.
  • Authentication and authorization get designed in from the start, while they’re still easy to change, rather than added once something is already in daily use.

AI Engineering & Intelligent Automation

Applying AI inside real engineering workflows: figuring out where an agent should have autonomy, where a human has to stay in the loop, and how to keep execution reliable.

Core capabilities

  • AI agent architecture & task decomposition
  • Tool-calling & multi-agent orchestration
  • Agent-assisted development with Claude Code
  • Model Context Protocol (MCP) integration
  • Prompt engineering & context management
  • Local LLM deployment & GPU inference

Technology ecosystem

AI AgentsClaude CodeModel Context ProtocolLocal LLMsComfyUI

Engineering considerations

  • An agent that can do a lot but can’t be trusted to stop when it should is a bigger risk than one that simply can’t do as much.
  • Human-in-the-loop checkpoints stay mandatory for anything consequential: deployments, deletions, anything touching production.
  • RAG, vector databases, and agent-security evaluation frameworks are current research interests here, without a production track record behind them yet.