Applied AI engineered for enterprise-scale outcomes
From architecture to adoption, I build systems that move real work.
My portfolio connects agentic AI, business applications, manufacturing intelligence, computer vision, quality, supply chain, and software-defined vehicles. Each platform is designed for production reliability, measurable value, and responsible human oversight.
The goal is not autonomous output. It is trusted intelligence that improves decisions, accelerates delivery, and transforms operations.
Context-first
Not prompt-first — assemble the business and technical context the work requires.
Work products
Architecture packages, investigation summaries, decision records — not chat demos.
Governance in-product
Approvals, audit trails, versioning, and validation gates are first-class.
Trust as engineering
Completeness, faithfulness, freshness, and precision as design criteria.
Flagship · Agentic intelligence layer
01
Rivian · AI Software DeliverySolution Architect
AISDLC
AI-powered software delivery with governance built in
Problem
Meaning fractures between requirement and implementation — designs drift, reviews arrive late, and teams duplicate work.
Solution
A coordinated multi-agent lifecycle that turns requirements into architecture, design, validation, and delivery-ready artifacts with approvals, version history, and end-to-end traceability.
My contribution
Shaped platform architecture, owned key intelligence and validation capabilities, contributed to gate-control logic, and defined how architecture decisions become traceable implementation work.
Design target
8-day
Target PRD → engineering cycle (14-day max)
99.5% orchestration availability (monthly target)
85%+ completeness · 90%+ precision on designated outputs
Usable architecture packages in ≥2 of 3 pilot projects
Rivian · Product Integrity · DefensePrincipal / Lead Architect
CLUE AI
Turning fragmented case intelligence into action
Problem
Investigators and legal teams assemble a complete picture from many systems before they can act — slow, repetitive, hard to scale, and inconsistent.
Solution
A governed retrieval and synthesis platform for citation-backed summaries, report generation, vehicle/case-scoped evidence, audit trails, and human confirmation before consequential updates.
My contribution
Shaped and stress-tested the architecture; established secure retrieval, AI analysis patterns, and human-reviewed workflow automation for high-stakes product-integrity work.
Rivian · Agentic RuntimeAgentic AI Solutions Architect
Rivian Agentic Platform
The foundation for scalable, governable AI agents
Problem
One useful assistant is easy; a family of reliable agents is hard — lost context, stale answers, unbounded loops, and inconsistent behavior across environments.
Solution
A control plane for agentic work: event-driven orchestration, explicit handoffs, bounded validation loops, versioned outputs, concurrency controls, and operational observability.
My contribution
Contributed to platform architecture, agent design, deployment coordination, access and infrastructure planning, and the control patterns that make agentic systems governable at enterprise scale.
Rivian · Manufacturing IntelligenceArchitect & Technical Lead
Weld PDM AI
04
From reactive alerts to predictive weld intelligence
Problem. Weld monitoring was reactive and fragmented — limited visibility into wire-feed degradation, clogged liners, arc instability, and process drift until failure made the issue visible.
Solution. Governed monitoring of weld trends, threshold alerts, fault visibility, and maintenance tracking — with a path to AI anomaly detection, diagnostics, and quality forecasting.
Contribution. Shaped data ingestion, platform architecture, machine-inventory mapping, analytical data design, dashboard direction, and the roadmap to AI-enabled weld diagnostics.
Design target
30–50%
Target downtime reduction (400+ min / month)
MVP surfaced detections including a feeder-liner defect
Target MTTA: ~45 min → <1 min
Target: 100% virtual inspection · up to 30% OpEx reduction
Rivian · Perceived QualityTechnical Lead & Solution Architect
PQ Apps
05
The digital source of truth for perceived quality
Problem. Excel- and email-based design-quality workflows do not scale — evidence disconnects from owners, approvals, and decisions.
Solution. A secure web source of truth for interface records, measurements, images, issues, actions, approvals, and 2D/3D section-map context in one traceable workflow.
Contribution. Shaped application architecture, identity and access, cloud environment, security direction, and the technical ownership model for the platform.
Rivian · Manufacturing DataSolution Architect & Technical Lead
ShimLog AI
06
AI-ready foundation for manufacturing fixture intelligence
Problem. Engineers rely on static 2D snapshots and disconnected historical files — hard to compare versions, validate entries, or trace decisions.
Solution. Interactive, searchable, version-aware 3D fixture experience with shim/spacer data, validation rules, history, file management, and reporting — a knowledge layer for future AI assistants.
Contribution. Shaped migration and exit strategy, requirements, solution design, architecture, integrations, knowledge transfer, and go-live readiness.
Design target
10–15%
Target productivity lift for designated workflows
Production launch with release, smoke, integration, and access checks
Foundation for AI-assisted troubleshooting and fixture-change analysis
Problem. Manual paint inspection is subjective, inconsistent, and hard to scale — defects are missed and recurring patterns stay invisible.
Solution. Controlled imaging plus computer vision to classify and trend paint conditions (blemishes, inclusions, thin paint), supporting review, analytics, and repair workflows with humans in the loop.
Contribution. Contributed AI strategy and use-case architecture for vision-based quality inspection and how visual intelligence supports manufacturing decisions.
Rivian · Supply ChainSolution Design & AI Roadmap Author
Supply Chain Automation
08
From manual reporting to resilient operations
Problem. Manual SAP reporting consumes expert time, creates error risk, and delays response to shortages, inventory shifts, and supplier risk.
Solution. Governed automation for recurring SAP reports, scheduling, monitoring, and exceptions — with a roadmap to forecasting, supplier-risk monitoring, and intelligent supplier communications.
Contribution. Authored the reporting solution design and the broader enterprise AI roadmap connecting automation, operational intelligence, and agent-based decision support.
A natural interface for the software-defined vehicle
Problem. As vehicles grow more capable, finding controls and learning features becomes complex — drivers need safer, hands-free interaction without diverting attention from the road.
Solution. Speech recognition, NLU, retrieval-grounded Owner’s Guide knowledge, voice responses, and deep vehicle integration — with safety guardrails, privacy controls, and confirmation for higher-risk actions.
Contribution. Architected design and delivery across AI orchestration, vehicle-system integration, platform connectivity, UX flows, and production-grade release requirements.
Design target
OTA live
In-vehicle experience via over-the-air release
Target: 30% of drivers use ≥1× per trip within 6 months
Target: 50% prefer voice for some interactions · NPS 90
Publishing note. KPI and ROI figures above are design targets or modeled value opportunities from platform criteria — presented as targets until validated with measured production results. Career timeline and prior roles: Experience.