Enterprise AI / Backend / Reliability Architect#
Production AI Systems for Complex, Mission-Critical Environments#
I help teams ship AI into production without increasing operational risk.
I work across AI workflow engineering, backend reliability, and legacy recovery, with observability and runtime validation built in from the start.
Best fit: teams that need AI features to work inside real systems, not isolated demos.
Market Fit#
I translate complex engineering work into market-recognizable roles: AI Systems Engineer, Backend Reliability Engineer, Platform Engineer, and Technical Lead for production systems.
I am strongest in environments that need someone who can stabilize operational risk, modernize legacy systems, and keep AI workflows measurable, auditable, and safe in production.
Search Keywords#
- AI Systems Engineer
- AI Platform Engineer
- Backend Engineer
- Platform Engineer
- Reliability Engineer
- Site Reliability Engineer
- Production AI
- Workflow Automation
- Runtime Validation
- Observability
- Distributed Systems
- Legacy Modernization
- Incident Response
- Token Optimization
- Multi-model Routing
What I Build#
- AI-assisted operational platforms
- Workflow automation systems
- High-reliability backend services
- Real-time distributed pipelines
- Runtime validation and observability infrastructure
- Migration-safe modernization layers
Engineering Principles#
- Reduce operational risk before adding feature depth
- Prefer observable, auditable systems over opaque ones
- Ship incremental change over high-risk rewrites
- Validate at runtime instead of assuming correctness
- Use AI to lower operational load, not hide failure modes
- Treat documentation as part of the system
Selected Work#
AI Workflow Optimization#
- Case study
- Problem: a degraded workflow and fragmented backend setup slowed delivery
- Outcome: reduced ETL latency from 70 seconds to 1.5 seconds through batching, routing, and production stabilization
AI Development Experience in Japan#
- Case study
- Problem: production AI work across real-time speech systems, workflow optimization, and LLM-assisted engineering needed a single coherent narrative
- Outcome: positioned the work as production AI engineering across automotive and enterprise systems
- Case study
- Problem: the team wanted to reduce reliance on a third-party IM provider and build a controllable realtime messaging core
- Outcome: designed a Go microservice IM platform with Kafka, EMQX, Redis, MySQL, MongoDB, and WebSocket services, supporting 16,000+ concurrent connections per node with sub-100ms latency
Frontend Stabilization Under Production Pressure#
- Case study
- Problem: an inherited Vue 3 and TypeScript IM frontend had forced logout, WebSocket instability, white screens, and precision bugs
- Outcome: stabilized 70+ critical issues within several days through runtime debugging, atomic patches, and test hardening
Legacy System Stabilization / Industrial Runtime Debugging#
- Case study
- Problem: a legacy SCADA platform built on Qt 4.7.3 and VS2003 had build-chain breakage, GDI-related crashes, and unstable telemetry visualization
- Outcome: recovered the build environment, improved runtime stability, and reduced operational troubleshooting cost while raising plotting capacity from ~30,000 to ~100,000 points
VC Experience / Windows Legacy Systems#
- VC experience page
- Problem: old Windows and embedded C++ systems needed a clear way to explain value beyond MFC maintenance
- Outcome: framed the work as legacy Windows recovery, ActiveX / COM boundary debugging, WinCE embedded security, and industrial modernization
SD-WAN Architecture Feasibility Study#
- Case study
- Problem: an SD-WAN control plane needed secure edge onboarding, clear interface modeling, and transport choices that could survive weak-network conditions
- Outcome: selected RabbitMQ, EMQX/MQTT, TLS-PSK, YANG, VPP/DPDK, and QUIC/BBR into a coherent cloud-control and edge-forwarding design
Dynamic Password Lock / Offline ATM Authentication#
- Case study
- Problem: an offline ATM lock needed an 8-digit dynamic password flow that resisted replay, cloning, and forged responses under embedded hardware constraints
- Outcome: designed a chained unlock protocol around prior close codes, hash-based state transitions, and low-overhead embedded cryptography
Spring Boot Data Ingestion Acceleration#
- Case study
- Problem: a Spring Boot 3 sports-data pipeline was too slow and brittle under HTTP polling, row-by-row persistence, and unstable third-party feeds
- Outcome: redesigned the ingestion path around push-style transport, batch upserts, and database indexing, cutting a 500-record sync from about 70 seconds to 1.5 seconds
Reliability Audit for Enterprise Booking Flow#
- Case study
- Problem: latency and bundle inflation were affecting a revenue-critical booking flow
- Outcome: identified concrete risk-reduction actions through an external reliability audit
Firmware and Protocol Boundary Audit#
- Case study
- Problem: a high-stakes financial protocol had a business-logic flaw and fragile trust boundaries
- Outcome: exposed an exploit path automated scanners missed and recommended institutional-grade controls
Deep Technical Archive#
For readers who want the low-level proof behind the positioning:
- Deep Technical Archive
- SM9 / pairing optimization / 13KB RAM
- Protocol reverse engineering / firmware boundary analysis
- Deterministic runtime design / constrained systems
Suitable Roles#
- Senior Backend Engineer
- AI Systems Engineer
- Platform Engineer
- Distributed Systems Engineer
- Site Reliability Engineer
- Staff Engineer
- Solutions Architect
- Technical Lead for Backend, Infra, or AI
Direct contact details are kept on the Contact page, so the homepage stays recruiter-friendly and easy to scan.
If your team needs someone who can make a complex system safer, clearer, and easier to operate, please use the contact page to continue.
I help teams integrate AI into production systems safely, with observability, runtime validation, workflow automation, and operational resilience. Direct contact details live on the Contact page.
AI Development Experience in Japan Executive Summary Senior Software Engineer with hands-on experience building production AI systems, LLM workflows, and AI-augmented engineering solutions across automotive and enterprise domains.
Focus Areas Production AI Systems LLM Integration AI Workflow Engineering Real-Time AI Applications AI-Augmented Software Engineering 1. Toyota AI Voice Sales Assistant Period: 2022.11 - 2023.01
Designed a real-time AI assistant for sales conversations. The system analyzed customer speech during the call and surfaced key signals such as:
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Distributed Real-Time IM Platform Executive Summary The company originally depended on a third-party IM provider. That made the business expensive to operate and limited how much the messaging experience could be customized.
I worked on a self-owned enterprise IM platform built with Go microservices so the team could control the stack directly and extend the system over time.
The target scope included:
1-on-1 chat Group chat File transfer Web real-time messaging Microservice architecture Future audio/video expansion 100,000+ user design target Sub-100ms message latency What I Built I co-designed the platform architecture and worked with the Tech Lead on the main service boundaries:
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OneKey Hardware Wallet Firmware Audit Architectural Review of Trust Boundaries, Build Reproducibility, and Secure Element Isolation Why I Investigated This This research originated from an independent review of wallet trust assumptions and supply-chain security models.
The goal was to evaluate whether the implementation matched the security guarantees presented to end users.
Security Research Process This work followed a responsible-disclosure workflow.
2026-05-11: findings were reported to the vendor security team 2026-05-12: related repository security policy updates were observed 2026-05-15: independent technical analysis was published Scope multiple firmware repositories build-chain analysis secure-element boundary review entropy validation review memory-boundary analysis Findings Overview 5 primary findings 4 boundary layers reviewed 3 publication touchpoints: Medium, CoinsBench, vendor disclosure Executive Summary This audit examines a mainstream hardware wallet firmware through an engineering lens rather than a marketing lens.
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AI Pipeline Re-Architecture Executive Summary A production AI workflow processing approximately 500 sports-analysis records per run had already proven business value.
The challenge was making it operationally scalable.
The original implementation required:
160-180 Minutes Per Run High LLM Operating Cost Frequent Workflow Failures Poor Scalability After re-architecting the pipeline:
~8 Minutes Per Run 16x-22x Faster ~99% Cost Reduction High-Reliability Operation The redesign transformed a prototype-grade workflow into a production-capable AI pipeline through throughput-oriented processing, workload-aware model routing, data-lineage preservation, and defensive reliability engineering.
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Legacy Platform Discovery & Modernization Executive Summary Supported a platform-modernization initiative involving the migration of a mission-critical foreign-exchange trading platform from HP-UX to Red Hat Enterprise Linux.
The primary challenge was not infrastructure migration.
The primary challenge was understanding a production system that had evolved continuously for more than twenty years, where documentation no longer reflected operational reality.
The migration could not safely begin until the system was understood.
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Toyota OMRM: Building a Hybrid-Cloud Real-Time AI Platform Executive Summary Designed and delivered a real-time AI-assisted sales coaching platform for a global automotive manufacturer.
The platform analyzed live customer conversations, generated in-call recommendations, and produced structured post-call records for training, quality assurance, and operational analytics.
Key capabilities included:
Real-Time Speech Intelligence Hybrid AWS + Azure AI Pipeline Speaker Attribution Intent Extraction Event-Driven Processing Serverless Scalability The primary engineering challenge was not speech recognition.
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Designing a Carrier-Scale Distributed Control Plane Executive Summary Designed the distributed control-plane architecture for a cloud-managed SD-WAN platform supporting large-scale edge deployments.
The project focused on solving several core distributed-systems challenges:
State Synchronization Control-Plane Scalability Device Identity Messaging Architecture Weak-Network Resilience Interface Governance The resulting architecture reduced control-plane synchronization traffic by approximately 90%, improved interface consistency across teams, and established a scalable foundation for cloud-managed network infrastructure.
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Merge-Sport: Spring Boot Data Ingestion Acceleration Project Overview I worked on a Spring Boot 3 / Java 17 sports-data integration platform responsible for synchronizing external match data into downstream services.
As traffic increased, the ingestion pipeline became the primary bottleneck. The system relied on HTTP polling, row-by-row persistence, and tightly coupled business logic, resulting in poor throughput, lock contention, and increasing operational risk.
The objective was not merely to improve performance, but to make the synchronization pipeline scalable, reliable, and maintainable for future growth.
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Offline ATM Security Lock with Dynamic Password Authentication Project Overview I designed and implemented the security and communication layer for an electronic lock used in ATM maintenance and controlled access workflows.
The system had to work in environments where continuous network access could not be assumed. In normal operation, the lock needed to generate and verify short-lived dynamic passwords. In initialization and maintenance flows, it also had to support secure host-device communication, device onboarding, and controlled key exchange.
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Stabilizing and Modernizing a Legacy Industrial SCADA Platform (Qt 4.7 / VS2003) 1. Project Context Around 2017, I worked on a large industrial SCADA platform used in the power and wind energy industry.
The system was built on Qt 4.7.3 and Visual Studio 2003, and it ran inside a legacy Windows environment with heavy operational constraints. The work was not a normal UI project. It was part of a larger production system that included realtime telemetry, backend services, industrial communication, and long-running operational clients.
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