Evidence
Engineering Work
Selected examples of solving ambiguous technical problems across enterprise deployment, production operations, systems design, and current AI engineering research.
Customer identities and confidential implementation details are withheld. Descriptions use anonymized scale and environment.
Turning a Deployment Into a Repeatable System
Pattern
The first successful implementation is the prototype. The reusable version — documented, templated, executable by others — is the deliverable.
Context
A large multi-site enterprise rollout required a repeatable implementation model that could scale across locations and delivery teams.
What I Did
- Worked with project and third-party delivery partner teams to establish a realistic production rollout cadence
- Designed, documented, and supported the initial 30 locations
- Created the implementation pattern used by broader deployment resources
- Coordinated provisioning and go-live support
- Introduced processes later reused across additional customer implementations
Outcome
- 6 locations per day, 4 production days per week
- One dedicated review/recovery day to close remaining issues
- Program held approximately 2 weeks ahead of schedule
Complex Enterprise Deployments
Pattern
Integrate into an environment you do not control: map the dependencies first, validate against the live system, and own the path to production acceptance.
Context
Complex enterprise voice, data, contact-center, SIP, routing, endpoint, and third-party integration deployments across multi-site customer environments, external carriers, third-party platforms, and internal engineering dependencies. At peak I carried roughly 50–80 customer projects concurrently and was assigned many of the highest-complexity implementations.
What I Did
- Technical discovery and requirements translation
- Solution design and voice/data provisioning
- SIP integration, routing logic, IVR, contact-center design
- Third-party integration and endpoint configuration
- Implementation, QA, UAT, and go-live support
- Escalation handling across internal and external teams
Outcome
- ~50–80 concurrent projects during high-load periods
- Ownership of high-complexity enterprise implementations
- >99% SLA compliance during major deployment work
- Significant installed recurring revenue contribution
Recovering a Business-Critical Production Outage
Pattern
Restore service first, then convert the incident into controls: change gates, validation steps, and failure isolation that prevent the same class of outage.
Context
A configuration change caused a large enterprise contact-center environment to fail, disrupting business-critical communications.
What I Did
- Engaged Support and Voice Engineering immediately
- Coordinated restoration across technical teams
- Held the incident bridge from approximately 5 PM until 3 AM
- Directed and supported technical recovery
- On site with the customer at approximately 6 AM
- Assisted disaster-recovery activities and restoration of distributed/remote users
Outcome
- Business-critical communications restored
- Distributed and remote users brought back online
- Cross-functional follow-up work to reduce recurrence risk
Engineering the Delivery System
Pattern
Recurring field failures are signal. Instrument them, then push them upstream into tooling, documentation, and product.
Context
Technical escalations and delivery workflows required clearer ownership, faster handoffs, better instrumentation, and more repeatable operating patterns.
What I Did
- Escalation intake, issue routing, and technical ownership
- Workflow visibility and delivery capacity instrumentation
- Resource allocation, team training, implementation standards
- Customer-risk prioritization and cross-functional coordination
Outcome
- A structured escalation workflow moved resolution from days to hours
- Recurring field and integration failures routed back to Product and Engineering
Luthier
Luthier is an ongoing research engineering project exploring how multiple AI providers, coding agents, local models, repositories, and infrastructure can operate concurrently while remaining observable, controlled, and provider-independent.
Research question
How can multiple autonomous AI tools safely work inside the same engineering environment without creating hidden state, collisions, provider lock-in, or uncontrolled execution?
Providers & routing
- Multi-provider orchestration (OpenAI, Anthropic, Google, OpenRouter, Together AI)
- Local models and provider abstraction
- Model routing, local/cloud routing, model fallback
Concurrency & state
- Concurrent agents over shared repositories
- Git-aware and filesystem state, collision detection
- Context management and structured agent configuration
Control & safety
- Capability boundaries, trust tiers, least privilege
- Policy-driven execution and failure containment
- Observability, traceability, deterministic configuration
Currently researching, designing, prototyping, testing, validating, and documenting the architecture and operating model.