Work
Operating large-scale platform reliability and rollout governance across 40M–70M+ devices — now applying the same deterministic control principles to AI safety, runtime governance, and enterprise AI infrastructure.
Scale & Impact
40M–70M+
Device-scale platform reliability exposure
12+
Years building large-scale reliability and rollout governance
50+ / 6
Engineers / orgs coordinated across release & triage workflows
28%
Reduction in repeat regression patterns across high-risk releases
5
AI trust infrastructure patent matters
Leadership Highlights
- Aligned 50+ engineers across 6 organizations under unified rollout governance models and cross-org execution.
- Regularly provided executive decision support to VP/SVP leaders on release risk and telemetry-driven triage for high-stakes platform releases.
- Current IP portfolio includes one U.S. application with Notice of Allowance and issue fee paid, three additional filed U.S. utility applications including one continuation-related application, and one additional application in preparation.
- Act as the operational bridge between platform infra, security, product, and leadership when making decisions that have real customer and regulatory impact.
Core Competencies
Reliability & Triage
Large-scale systems reliabilityTelemetry-driven triageIncident reviewRegression detectionAnomaly thresholds
Rollout & Governance
Rollout governanceRelease risk assessmentGo/no-go gatesCompliance coordinationExecutive decision support
AI Trust Infrastructure
Deterministic control principlesRuntime governanceProvenance systemsPolicy enforcementDecision replay
Case Studies
Case Study 1 – Rollout Governance at 40M–70M+ Device Scale
Turning ad-hoc rollout decisions into structured release risk gates.
- Context & Challenge
- Firmware and feature releases across tens of millions of gateways were historically gated by fragmented telemetry, leading to unquantified release risk.
- Execution
- Defined rollout governance structures, telemetry-driven triage workflows, and risk thresholds; aligned engineering, QA, and operations on a unified framework; provided executive decision support for high-stakes releases.
- Outcome
- Achieved a 28% reduction in repeat regression patterns across high-risk release cohorts and gave VP/SVP leaders a clear, quantitative view of safety vs. velocity.
Case Study 2 – Bridging Reliability to AI Trust Infrastructure
Applying deterministic control principles to enterprise AI safety.
- Context & Challenge
- AI governance often lacks the rigorous, inspectable infrastructure seen in traditional large-scale systems reliability.
- Execution
- Designed reusable, deterministic rule-engine patterns for code safety, policy enforcement, and risk assessment; bridged proven release risk methodologies into the AI domain.
- Outcome
- Created independent deterministic AI trust infrastructure blueprints that translate large-scale reliability principles into replayable, auditable, policy-bound enterprise AI control systems.
Career Timeline
January 2024 – Present
Independent Researcher — AI Trust Infrastructure & Deterministic Systems · Camden, NJ
Independent product and architecture research on deterministic infrastructure for enterprise AI: verifiable behavior, provenance, decision replay, runtime execution control, and deterministic code remediation.
- Developed an independent research portfolio focused on AI trust infrastructure for enterprise systems operating in regulated, distributed, and high-consequence environments.
- Designed a five-system Enterprise AI Trust Stack covering deterministic code authorization, offline remediation, decision replay, cross-modal provenance, and runtime execution control.
- Advanced research on moving AI safety from post-hoc review into runtime infrastructure that is auditable, replayable, gated, and provenance-aware.
- Built prototype concepts and technical frameworks that translate reliability principles from connected-device and broadband systems into enterprise AI trust architecture.
- Created technical writeups and video briefings explaining AI trust infrastructure, deterministic systems, runtime governance, and enterprise AI product decision-making.
- IP portfolio includes one U.S. patent application with Notice of Allowance and issue fee paid, three additional filed U.S. utility applications including one continuation-related application, and one additional application in preparation.
2018 – Present
Comcast — Product / Technical Lead · Philadelphia, PA / Remote
Lead reliability analysis, telemetry-driven decision support, release validation, and operational integrity workflows across broadband and Wi-Fi platform environments. Since Q3 2025, focused on broadband field triage, RDK-B behavior, Wi-Fi system issues, release validation, and customer-impact risk across a 40M+ broadband device footprint.
- Lead telemetry-driven triage workflows to identify and mitigate regression detection across broadband platforms.
- Monitor RDK-B behavior, state synchronization, VAP/BSSID behavior, and memory pressure to ensure operational integrity.
- Manage release validation and rollout governance, making go/no-go decisions based on customer-impact risk.
- Provide executive/stakeholder updates on platform health, release safety, and cross-org execution progress.
2014 – 2018
Systems & Network Engineering Roles
Built and operated networked systems, laying the operational foundation for later work in large-scale platform reliability, observability, and deterministic control.