I design and build reliable, intelligent, and highly optimized software products from low-level systems, IoT telemetry, and custom search engines to production-grade AI/RAG pipelines.
Over the past 15+ years, I have architected, deployed, and maintained full-stack systems across industries like healthcare, green energy, beauty tech, and industrial IoT. My focus is on writing memory-efficient, low-latency code and scaling infrastructure that lasts.
- Languages: Python, Node.js / TypeScript, C / C++, SQL.
- Architectures & Systems: Distributed Systems, Event-Driven Architecture, Microservices, IoT Telemetry, Embedded Interfaces.
- AI, ML & Search: Vector Search & Custom Full-Text Search, LLM Pipelines & Evaluatable RAG, Computer Vision, Classical ML.
- Databases & Messaging: MySQL, PostgreSQL (PostGIS, TimescaleDB), MongoDB, Redis, MQTT (NanoMQ, Mosquitto), Modbus.
- DevOps & Infrastructure: AWS (EC2, CloudWatch, SSM), Docker, PM2, GitHub Actions, Linux Administration.
An extremely compact, immutable, and memory-optimized precompiled full-text search index fork of MiniSearch.
- Focus: Ultra-low RAM footprint, zero-dependency, binary snapshot support, and heavy performance benchmarking.
- Tech: TypeScript, Algorithmic Tries, Levenshtein Distance, Benchmarking Suites.
A robust, industrial-grade Modbus-to-MQTT gateway designed for reliable telemetry ingestion in production environments.
- Focus: Zero-leak memory profile, connection resilience, and rapid payload parsing.
- Tech: Python, Modbus Protocol, MQTT Client Benchmarking.
An open-source pipeline transforming public French government drug databases into an easily queryable REST API.
- Tech: Node.js, Data Normalization, API Design.
I serve as a Software Architect and Lead Developer for complex business platforms, managing products from conception to high-load production.
Architected the backend and telemetry ingestion pipelines for a real-time energy monitoring dashboard used in commercial buildings and hospitality networks.
- Achievements: Scaled timeseries ingestion (MQTT, Modbus, Zigbee) into PostgreSQL/TimescaleDB. Integrated native utility APIs (Enedis/GRDF).
- Tech Stack: Python, SQLAlchemy, Celery, TimescaleDB, MQTT.
Designed and deployed production-grade RAG systems and structured document extraction pipelines for the pharmaceutical and medical sectors.
- Achievements: Built a conversational RAG assistant using a dual-model pipeline. Engineered document processing APIs for pharmacy automation, handling multi-format data ingestion, medical transcription, and structured data extraction from clinical PDF forms.
- Tech Stack: Node.js, Python, Qdrant Vector DB, LLM APIs, PDF Parsing Engines[cite: 1].
Engineered the core API and cloud infrastructure for a computer vision platform analyzing skin and hair conditions from mobile-uploaded photos.
- Achievements: Built scalable, asynchronous image processing pipelines on AWS. Developed robust operational monitoring (SSM, CloudWatch alerts) to guarantee 99.9% availability.
- Tech Stack: Python, PyTorch/OpenCV integrations, AWS SSM/CloudWatch, Docker.
My approach to engineering is rooted in a strong scientific foundation (Master's degree in Computer Science and early research work in Classical ML/SOMs).
I believe that:
- Performance is a feature: A 10x reduction in memory or CPU usage translates directly to infrastructure savings and a better user experience.
- Telemetry is mandatory: A system is only as reliable as its monitoring. If it is not monitored, it is broken.
- Simplicity scales: Prefer boring, robust technologies (Postgres, Redis, clean Unix/Linux pipelines) over complex distributed state unless strictly necessary.
- LinkedIn: linkedin.com/in/joshua-melka