Backendsystemsengineerwithproduct-levelownership.

Problem & context

Modernized core healthcare SaaS backends for reception, reservation, payment, digital charting, patient lookup, and hospital configuration into consistent Kotlin Spring Boot APIs.

Engineering decisions

• Standardized clinical chart and reference flows, including prescriptions, diagnoses, templates, consent forms, edit locks, and delete logs, with OpenAPI and exception documentation.
• Hardened reservation and reception integrations with broad-query guards, linked reservations, department/doctor/date counts, device settings, receipt creation, and patient-app wait-list notifications.

Evidence & outcomes

• Stabilized payment and treatment-room operations through transactional approval/cancel/refund/credit paths, card-terminal history, receivable checks, socket waiting screens, voice guidance, assignment statistics, and rollback paths.

Problem & context

Helped move a healthcare SaaS service fleet from single-service deployment thinking into Kubernetes and ArgoCD-managed application releases.

Engineering decisions

• Worked with Helm charts for patient-facing APIs, hospital admin APIs, automatic reception, payments, waiting displays, procedure allocation, charting, scheduling, interoperability, and shared Spring Boot service templates.
• Standardized Kubernetes templates for deployments, services, ingresses, security groups, HPA, PDB, KEDA ScaledObject, Istio VirtualService, and ServiceMonitor coverage instead of one-off YAML.

Evidence & outcomes

• Supported observability and operations manifests around kube-prometheus, Grafana, Alertmanager, node-exporter, kube-state-metrics, Datadog agent, Jaeger, Kafka/Debezium, Karpenter, and ArgoCD-managed releases.

Problem & context

Research-first quant pipeline for MES/ES intraday opportunity selection, turning Databento OHLCV and MBP feeds into clean features, labels, deterministic baselines, and model-ready validation gates.

Engineering decisions

• Built the market-data path from raw Databento files into bronze/silver Parquet datasets, strict training matrices, chronological splits, manifests, and reproducible research reports.

Evidence & outcomes

• Designed the research gate around feature information-coefficient reports, strategy-family benchmarks, horizon and roll-rule sensitivity, lookahead audits, MBP microstructure diagnostics, latency/fill/cost stress, and candidate failure attribution.

Problem & context

Research system that turns Borg-inspired workload traces and live Kubernetes telemetry into risk and demand predictors, multi-agent remediation decisions, and dual-cluster comparison evidence.

Engineering decisions

• Built the six-layer path from Prometheus/JSON ingestion and simulator feature extraction to XGBoost risk/demand models, RLlib PPO policy evaluation, Optuna reward tuning, and scoreboard feedback.

Evidence & outcomes

• Validated the control loop on local Kind clusters with AIOpsLab-style incidents, Prometheus/node-exporter telemetry, HPA/local-Karpenter baselines, live Kubernetes action execution, and repeated-seed policy gates.

Problem & context

Built a native, local-only macOS application that connects live TCP/UDP and Docker evidence to reviewed service intent, ownership-aware lifecycle control, dependency-aware projects, and restorable workspace sessions.

Engineering decisions

• Separated disposable runtime observations from versioned managed-service definitions, then guarded every external action with strong process fingerprints, listener-ownership checks, protected-process rules, and exact Docker/Compose routing.
• Exposed the same application-owned control plane to Codex through an MCP 2025-11-25 STDIO helper and current-user Unix socket, with stable IDs, optimistic revisions, secret-safe results, and single-use destructive previews.

Evidence & outcomes

• Shipped 82 production MCP tools and validated the native app with 210 passing tests; matched Release measurements reduced closed-window CPU from 69.2% to 0.118% over 901 seconds.

Problem & context

Near-startup proptech product that helps founders evaluate Seoul trade areas and shortlist commercial property listings through score-driven analysis, map workflows, and recommendation APIs.

Engineering decisions

• Built backend and frontend surfaces for map-based trade-area analysis, property search, Top 3 recommendations, saved analyses, and detailed listing inspection.
• Implemented API flows around analysis creation, polling and SSE progress, property filtering, structured prompt search, horizon survival scores, score explanations, and metric references.

Evidence & outcomes

• Shaped product UX across Analyze, property listings, Detail, History, shortlist/compare, budget filters, and natural-language property exploration; the project went on to win the 2026 Seoul Big Data Utilization Contest Grand Prize.

Problem & context

Award-winning travel mobile product that connects travelers and locals through trip cards, city-based place discovery, nearby mingles, quick matches, saved places, and translated chat.

Engineering decisions

• Built Kotlin Spring Boot APIs for trips, city and nationality lookup, restaurants, cafes, saved places, mingles, quick matches, chat rooms, and profile image upload.
• Implemented React Native screens and service integration across Home, Nearby, QuickMatch, Chats, trip history, saved places, profile, and trip creation flows.

Evidence & outcomes

• Supported realtime STOMP chat and quick-match events, unread states, OpenAI-backed message translation, and bilingual UI/API behavior.

Problem & context

Built a healthcare messaging backend around Socket.IO/Netty, Kafka events, Redis/Redisson state, JPA/Liquibase persistence, translation, media, tags, reactions, friends, hospitals, and read offsets.

Engineering decisions

• Implemented controllers and services for chats, messages, participants, read state, history, media, reactions, tags, friends, users, and user-hospital unread counts.
• Handled realtime delivery through Socket.IO room subscriptions, chat-message, typing, and read-receipt events, then fanned persisted messages out through Kafka producers and consumers.

Evidence & outcomes

• Added launch-support surfaces around translation configuration, AWS Secrets Manager and S3 integration, Datadog tracing, Slack notifications, request logging, and quiet socket error handling.

Problem & context

Built data and operations pipelines that made product usage and business metrics visible outside the app runtime across patient-app, review, campaign, credit-balance, and hospital operations flows.

Engineering decisions

• Prefect flows collected daily and monthly metrics from Aurora MySQL, BigQuery/GA, Redis, AWS utility modules, and service SQL modules, then published Datadog metrics for dashboards.
• Maintained auto-discovery deployment registration so @flow entrypoints were pushed to the EKS work pool with namespace, service account, image, image tag, and pull-policy configuration.

Evidence & outcomes

• Built backfill, tag-rewrite, metric-history export, and dashboard-update scripts so historical KPI series could be replayed without breaking existing graphs.

Problem & context

Built a Korean-learning product through SWMaestro, covering ideation, planning, backend development, deployment, and app launch.

Engineering decisions

• Developed video-processing flows for subtitle-based learning and speaking practice.
• Used NLP and OpenAI APIs to extract vocabulary examples and usage context from learning material.

Evidence & outcomes

• Owned product execution across backend APIs, Flask services, nginx deployment, and launch preparation.

Problem & context

Turned repeated integration friction into explicit platform patterns across backend, messaging, observability, and deployment repos rather than leaving each issue as a local fix.

Engineering decisions

• Standardized hospital-scoped request context through argument resolvers, Feign auth-token clients, Secrets Manager and Parameter Store configuration, and multi-module application/domain boundaries.
• Improved operational visibility with Swagger/OpenAPI customizers, exception reason docs, request/response logging, Slack error routing, Datadog tracing/metrics, ServiceMonitor coverage, and Prometheus/Grafana manifests.

Evidence & outcomes

• Hardened async and integration paths around SQS, Kafka/Debezium, Redis/Redisson, KEDA autoscaling, PDB/HPA resilience, and transactional facades for payment, reservation, receipt, and allocation flows.