End-to-End Product Ownership
I manage the full lifecycle: from initial architecture and coding to on-site deployment and post-launch stabilization.
Open to OpportunitiesFull-Stack VersatilityOpen-Source Builder
ALI BOYACIFull-Stack Product Engineer
Istanbul, Türkiye · Remote-Friendly · 4+ Years
Production-grade systems — from industrial automation to open-source tooling.
I build and ship across the full stack: on-prem deployments, open-source CLI tools, B2B SaaS platforms, and mobile apps. I own the entire lifecycle — architecture, implementation, deployment, and monitoring — ensuring systems thrive in production.
Expertise at a Glance
- DomainIndustrial Automation, SaaS, Dev Tools & Mobile
- OwnershipArchitecture → Development → Deployment → Monitoring
- ImpactProduction stability, test discipline, measurable outcomes
Core Strengths
Built for Production
Engineering decisions driven by operational reliability and performance.
Real-Time System Architecture
Extensive experience with MQTT/WebSocket pipelines, designed for low-latency and high-reliability in edge environments.
Operational-First UX
Developing intuitive kiosk and reporting interfaces that minimize human error in high-pressure environments.
Risk-Mitigated Deployment
I don’t just "push to prod." I deliver with robust runbooks, rollback strategies, and comprehensive monitoring.
Experience
Professional Journey
A track record of delivering stable software in unstable environments.
- Architected Node.js microservices and integrations for industrial MES/IoT ecosystems.
- Engineered real-time monitoring dashboards and React-based kiosk panels for field operations.
- Led on-prem deployments and on-site stabilization for mission-critical production lines.
Case Studies
Real-World Deliverables
Detailed breakdowns of technical challenges and engineered solutions.
DriftGuard — Open-Source Schema Drift CLI
An enterprise-grade Python CLI that detects schema and data contract drift across databases, APIs, and files before breaking changes reach production.
- 7 source collectors: PostgreSQL, MySQL, SQLite, OpenAPI, JSON Schema, CSV, YAML
- Semantic diff engine with 12 event types and fuzzy field rename detection
- 190 tests, 78% coverage, CI validated across Python 3.11 / 3.12 / 3.13
Collectors→
Normalizer→
Snapshot Store→
Diff Engine→
Policy Engine→
Reporter→
CI Gate
Details
Context
Most schema validation tools focus on a single data layer — SQL linters check migration syntax, API linters check OpenAPI specs. Real drift happens across layers: a renamed Postgres column can break a downstream CSV export or a partner API consumer. DriftGuard was built to catch these cross-source breaking changes in one unified pipeline.
Constraints
- Must normalize schemas from 7+ heterogeneous sources (databases, APIs, files) into a single comparison model.
- Policy enforcement must be configurable per-team: 5 modes (strict, lenient, default, backward-compatible, forward-compatible).
- Must integrate as a CI gate with non-zero exit codes on policy violations.
Technical Implementation
- Collector architecture with 7 adapters behind a common interface (SQLAlchemy 2.x for databases, pyarrow for files, HTTP clients for APIs).
- Semantic diff engine: type widening taxonomy, constraint-level diffing (PK, FK, unique, numeric ranges), and fuzzy field rename detection via SequenceMatcher.
- Policy engine with 5 enforcement modes and per-resource severity overrides.
- Multi-format reporters: Terminal (Rich), JSON, Markdown, HTML.
- Full test suite: 190 tests, 78% line coverage, ruff + mypy static analysis, GitHub Actions CI matrix.
Outcomes
- Published as open-source on GitHub with comprehensive documentation (architecture guide, CLI reference, adapter guide, policy rules).
- CI pipeline validated across Python 3.11, 3.12, and 3.13 via GitHub Actions.
- Extensible architecture: new collectors and policy modes plug in without changes to the core diff engine.
Future Roadmap
- Add collectors for Avro, Protobuf, and Kafka Schema Registry.
- Implement SARIF and JUnit XML reporters for CI/CD integration.
Mission-Critical Automation System
A comprehensive on-prem solution for a high-throughput facility, driving operational excellence from system architecture to field rollout.
- Zero-downtime rollout within a live-ops environment
- Unified ownership of Backend, UI, and Infrastructure
- Defined KPI baselines and instrumentation for operational monitoring
Project Impact
Complete
Ownership
Architecture → Rollout
On-prem
Environment
Field-tested stability
KPI-Driven
Focus
MTTR & Throughput focus
Details
Context
Developed for a high-capacity loading facility requiring absolute on-prem stability and real-time data processing without cloud dependency.
Constraints
- Strict on-prem infrastructure with no external cloud access.
- Implementation required during active, 24/7 operations.
- High data density with low-latency requirements.
Technical Implementation
- Node.js rule engine for processing complex device events.
- Ergonomic React interfaces designed for rugged field use.
- Custom deployment automation tailored for restricted on-prem environments.
- Oncall rotation, runbooks, and incident response playbooks for production stability.
Outcomes
- Digitized manual workflows, significantly reducing operational error rates.
- Established a scalable technical foundation for future industrial IoT expansions.
- Stabilized go-live through meticulous incident response planning.
Future Roadmap
- Integrate predictive maintenance modules using existing KPI data.
- Expand failover drills and automated load testing.
ORIA (B2B Workflow Automation)
A solo-built MVP featuring WhatsApp-first CRM integration and AI-augmented workflow management.
- Full-cycle MVP delivery from conceptualization to beta
- Direct WhatsApp-to-CRM data mapping and automation
- AI-driven draft generation to reduce manual operational overhead
WhatsApp Intake→
Data Processing→
AI Generation→
Human Approval→
CRM Sync
Details
Context
A stealth-mode startup focusing on streamlining B2B communication through familiar interfaces like WhatsApp.
Constraints
- Tight turnaround for MVP delivery to initial beta testers.
- Requirement for high data privacy and audit trails.
Technical Implementation
- Core product architecture using Node.js and PostgreSQL.
- Integration with WhatsApp Business API for seamless communication.
- Administrative dashboard for process moderation and analytics.
Outcomes
- Successfully delivered closed beta to initial corporate partners.
- Reduced manual data entry time by approximately 60% through AI-assisted drafts.
Future Roadmap
- Implement advanced reporting for activation and retention metrics.
- Harden permission levels for enterprise-grade security.
Wardrobe — AI-Powered Outfit Planner
A React Native mobile app that provides AI-powered outfit recommendations based on weather forecasts and personal wardrobe data.
- Photo-based garment management with AI-powered analysis and categorization
- 7-day weather forecast integration for context-aware outfit planning
- Cross-platform TypeScript app with Supabase backend (Postgres + Auth + Storage)
Project Status
iOS & Android
Platform
Expo + React Native
Supabase
Backend
Postgres + Auth + Storage
Beta
Status
Private repo
Details
Context
A personal project exploring AI-driven personalization in a consumer mobile context. Users photograph their wardrobe items; the app analyzes garments and recommends outfits aligned with weather conditions and personal style.
Constraints
- Image processing and AI analysis must stay responsive on mobile hardware.
- Personalization engine must learn from user feedback without excessive data collection.
Technical Implementation
- Expo + React Native + TypeScript with TanStack Query and Zustand for state management.
- Supabase integration: Postgres with Row-Level Security, Auth, and Storage for garment photos.
- Open-Meteo weather API integration for 7-day forecasts with location-based context.
- Sentry for error tracking, PostHog for product analytics, i18next for Turkish/English localization.
Outcomes
- Functional beta with batch garment upload and AI-powered garment categorization.
- Personalization engine that improves suggestions based on user selections over time.
Future Roadmap
- Refine recommendation algorithm based on beta tester feedback.
- Add outfit history and social sharing features.
What I Bring
Strategic Value
Moving beyond features to deliver scalable business outcomes.
Mission-Critical On-Prem Delivery
Architecture and implementation designed for reliability in air-gapped or restricted environments.
High-Trust Operational Dashboards
Real-time, latency-aware interfaces that teams actually rely on for daily tasks.
Versatile Technical Delivery
From Python CLI tools to React Native apps, from on-prem automation to SaaS — adapting architecture to the problem, not the other way around.
Risk-Managed Release Cycles
Controlled deployments backed by testing, documentation, and incident response readiness.
Contact
Let's Connect
Looking for a product-minded engineer? I typically respond within 24–48 hours.
Email is the preferred channel for initial outreach.
aaliboyaci@gmail.com