Applied AI / LLM Engineer

Remote product IT company. Technical owner of the LLM layer across client projects: RAG system design, retrieval and embedding tuning, prompts, output control, model selection and routing, LLM API integration, and monitoring. Designed a support and analytics platform with semantic search across 4 data streams; directed embedding-model migration and improved retrieval quality to 0.82 from 0.59. Designed an AI sales and negotiation coach and wrote its development spec. Designed the rebuild of an assistant-platform bot into a single AI agent. Led decomposition, backlog, roadmap, sprints, specs, estimates, and acceptance across projects.

Integrated LLMs into existing products and built Telegram bots from scratch with AI integration for small-business and founder clients under NDA. Completed intensive independent study of LLMs, RAG, agent design, and inference-cost optimization, including a local-LLM sandbox on networked Raspberry Pi.

Designed the prompt layer and knowledge base for an LLM support assistant that autonomously resolves about 73% of L1 tickets. Built device telemetry across a 10k+ device fleet, Grafana dashboards, and Telegram alerting. Performed QA, smoke, regression, and A/B testing; handled edge-device flashing over SSH; mentored a mid-level engineer.

Worked in L2 support and monitoring on a high-traffic real-time platform with about 15-minute response SLA. Used Graylog, Grafana, Telegram alerts, and REST API diagnostics. Also handled L1-L3 support, project management, CI from the support side, omnichannel B2B/B2C support, manual QA, moderation, and built Chrome extensions and knowledge bases.