Workflow Engineering
Lead flow, estimating, follow-up, approvals, handoffs, task queues, content operations, and operational state made explicit.
Forward Deployed Engineer work for teams that need AI implementation inside real operations: agentic systems, workflow automation, integrations, MCP-enabled tooling, operational software, and production deployment.
My approach to AI and software comes from field work: observe what actually happens, find the bottleneck, build at the seam, test against reality, and leave behind something the operator can use. The title matters less than the operating posture: forward-deployed, cross-functional, and accountable to the workflow.
Lead flow, estimating, follow-up, approvals, handoffs, task queues, content operations, and operational state made explicit.
Research, decomposition, drafting, implementation, review, monitoring, and action routed through governed agent workflows.
APIs, webhooks, CRM seams, email, messaging, databases, vendor tools, and replaceable adapters instead of brittle one-off glue.
Dashboards, founder queues, status surfaces, command centers, and human-readable views of what the system is doing.
Receipts, verification, source validity, approvals, rollback, and the difference between a claimed action and a proven external effect.
Edge runtime, databases, observability, health checks, preview environments, and production hardening that turns code into an operating capability.
The work is treated as a governed sequence, not a bag of tasks.
Watch the real workflow and collect evidence before deciding what the software should be.
Build the smallest high-leverage seam that changes the operator's reality.
Test the loop end to end, including the external effect and the failure path.
Standardize what worked so the next workflow gets cheaper, faster, and more reliable.