Technical Delivery · Professional Services · Customer Lifecycle
Technical Deployment & Professional Services Leader
Forward-Deployed Engineering | Solutions Architecture | Enterprise Delivery
I lead where commercial commitments, systems architecture, and production delivery meet.
Across 15+ years in telecom and enterprise Professional Services, including UCaaS/CCaaS, I advanced from NOC operations and complex contact-center systems design to global leadership spanning pre-sales, implementation, adoption, and growth.
My differentiator is full-lifecycle technical delivery leadership: turning customer needs and commercial commitments into working, supportable systems, then building the standards, operating models, escalation paths, and feedback loops required to scale them.
How I work
Engineering at the customer boundary.
The hardest technical problems rarely arrive as clean tickets or greenfield projects. They arrive as incomplete requirements, legacy systems, production dependencies, security constraints, third-party integrations, deadlines, and customers who need the system to work.
Discover
Understand the customer objective, current environment, requirements, constraints, dependencies, feasibility, risks, and success criteria.
Design
Translate the requirement into solution architecture, integration scope, delivery sequencing, acceptance criteria, and an operating model.
Align
Coordinate Product, Engineering, Sales, Solutions Engineering, Project Management, Customer Success, Support, partners, carriers, and customer stakeholders.
Implement
Configure, integrate, migrate, test, validate, document, and prepare the complete customer environment for production.
Operate
Support cutover, go-live, hypercare, adoption, change management, incident response, renewal, and expansion.
Scale
Turn field experience into repeatable standards, staffing models, escalation paths, metrics, tooling, documentation, governance, and Product or Engineering feedback.
Current Applied AI & Systems Practice
AI is the next customer-delivery environment.
AI is not a departure from this career; it is the next customer-delivery environment. The systems behind it may be complex, but the customer experience should be as simple and reliable as dialing a phone.
My current hands-on work spans generative AI and LLM integration, APIs, RAG, MCP, provider-neutral model routing, agent workflows, evaluation, local inference, relational and vector data, and Linux/containerized environments.
The delivery obligation remains familiar: understand the environment, define the outcome, design the integration, validate the complete system, support production, and turn what works into a repeatable pattern.
Career arc
Built from the field inward.
Every layer of the stack, learned in the order things actually break.
- 01
Technical Support / NOC
Break/fix, MACD, production monitoring, network troubleshooting, customer-impacting incidents.
- 02
Voice & Data Engineering
Routing, SIP, voice/data services, contact-center systems, endpoints, customer-premise environments, escalation.
- 03
Enterprise Deployment
Discovery, design, provisioning, integrations, QA, UAT, implementation, go-live.
- 04
Professional Services
Complex deployments, escalation ownership, repeatable implementation processes, partner coordination.
- 05
Global Technical Delivery Leadership
Large implementation portfolios, operating models, staffing and capacity, escalation paths, Product/Engineering feedback loops.
- 06
Current Applied AI & Systems Practice
LLM integration, APIs, RAG, MCP, model routing, agent workflows, evaluation, local inference, relational and vector data, Linux and containers.
Evidence
Production experience, not just architecture.
15+ Years
Customer-facing technology
Support, network engineering, enterprise deployment, and delivery inside live production environments.
50–80
Concurrent deployments
Carried during hands-on implementation work, weighted toward high-complexity projects.
>99%
SLA compliance
Held across complex multi-site enterprise deployments during major implementation work.
$20M–$30M
Delivery portfolio
Enterprise implementation portfolio under delivery ownership across distributed customer environments.
33%
Reduced delivery timelines
Improved delivery predictability through KPI-driven operations and continuous-improvement frameworks.
30%
Reduced project overruns
Improved delivery predictability through KPI-driven operations and continuous-improvement frameworks.
Now
Current Applied AI & Systems Practice
Independent engineering and research across Go, Python, and Rust; contract-first APIs and OpenAPI; relational and vector data; Linux and containerized infrastructure; local and cloud inference; model routing; agent coordination; observability; automation; and developer tooling.
Current projects are being developed, tested, documented, and prepared for public release.
Independent Research Engineering Case Study · Ongoing
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.
Read the research notesContact
Let’s talk.
I am focused on Technical Deployment and Professional Services leadership roles with growth-stage organizations building or scaling Forward-Deployed Engineering, Solutions Architecture, Enterprise Delivery, and technical Customer Success functions.