- ?Who is Grygorii Kylypko?
- Grygorii Kylypko is a full-stack engineer, platform architect and tech lead based in Kharkiv, Ukraine, with 15+ years of commercial experience. He designs and delivers production systems built on Node.js, React and TypeScript, and works remotely with distributed product teams.
- ?Is Grygorii Kylypko available for hire?
- Yes. He takes on contract engagements as Tech Lead, Platform Architect or principal full-stack engineer, working remotely with distributed teams. The fastest way to start a conversation is email: hello@kylypko.com.
- ?What is his core technology stack?
- Node.js, React and JavaScript/TypeScript at the core, with PostgreSQL, MongoDB and Redis for storage. Systems are deployed as cloud-native microservices on AWS and Azure using Docker and Kubernetes, exposed over REST, GraphQL and WebSockets, and observed with Prometheus, Grafana and ElasticSearch/Kibana.
- ?What is his experience with EV charging and the OCPP protocol?
- Since 2023 he has been building a billing and management platform for electric-vehicle charging networks on OCPP 1.6J and 2.0.1. It covers charge-point provisioning, real-time charging-session management over WebSockets, energy metering, tariff-based billing and operator and driver dashboards.
- ?What is his experience with solar energy and IoT?
- More than five years monitoring solar power plants, fields and inverters — tracking energy produced and exported to the grid. That includes automated monitoring of Ukraine's day-ahead electricity market with automated sale and export decisions, and energy-metering-node monitoring with automatic efficiency calculations surfaced through custom CRM and admin dashboards.
- ?Does he work with AI and LLMs?
- Yes. Since 2023 he has designed and delivered AI/LLM solutions for organisations: RAG assistants over corporate knowledge, AI agents and MCP (Model Context Protocol) servers connected to internal systems, document-processing automation and LLM analytics over business and IoT data. He works with OpenAI, Anthropic Claude and Google Gemini APIs as well as open-weight models served through Ollama, vLLM and LM Studio, and uses AI-assisted engineering tools daily.
- ?What AI/LLM solutions does he build for organisations?
- Typical deliverables: a retrieval-augmented generation (RAG) assistant that answers from internal documentation, tickets, contracts and CRM data with cited sources; AI agents and MCP servers that let an LLM safely operate company APIs and databases with role-based access and audit logs; automated document processing (OCR + LLM extraction and validation of invoices, contracts and reports); natural-language analytics over databases and telemetry; and the evaluation, guardrails and cost-control layer that keeps these systems reliable in production. Everything is delivered as Node.js/TypeScript services on AWS, Azure or on-premise.
- ?Can he deploy LLMs on-premise without sending data to the cloud?
- Yes. For organisations with data-residency or privacy constraints he deploys open-weight models on company servers or in a private VPC using Ollama, vLLM or LM Studio, with vector search on pgvector or Qdrant. Where policy allows, a routing layer can send non-sensitive requests to cloud models (OpenAI, Anthropic, Gemini) and keep sensitive ones local, balancing quality, latency and cost.
- ?What services does he offer?
- Five main areas: AI/LLM solutions for organisations (RAG, private LLM deployments, AI agents and MCP integrations, document automation), e-mobility solutions (EV charging, OCPP billing and IoT), architecture-first custom software development across the full lifecycle, web development with Node.js and React at scale, and mobile development with React Native plus the backend APIs behind it.
- ?Which companies has he worked with?
- Engagements include PLUTO.TV, H&M in Berlin, Bosch, Heartland Commerce/Xenial, AstrumU, IDEX Exchange, NOMO Bank and SocialBakers, alongside AI/LLM solutions for organisations and EV-charging, solar-energy and video-intelligence platforms. The full history is on this page and in the machine-readable CV at /resume.json.