I build streaming-first backends and mobile apps that are easy to operate: clear APIs, solid tests, and dashboards that tell the truth. Lately I’ve focused on LLM-powered features with measurable latency, cost, and reliability.

• Backend: Python (FastAPI, AsyncIO, Pydantic v2), SQLAlchemy 2.0, Celery, PostgreSQL, Redis
• AI: OpenAI API, pragmatic RAG (Qdrant/pgvector), MLflow experiments, local models via Ollama / llama.cpp / vLLM
• Ops: Docker, GitHub Actions, Prometheus + Grafana, OpenTelemetry tracing, Sentry
• Mobile: Flutter (Dart, BLoC/Cubit, Provider, auto_route), Android/Kotlin (incl. BLE/GATT)

A public PoC that shows senior backend practices + LLM orchestration in one repo.

• FastAPI service with JWT multi-tenancy, typed contracts, and SSE/WebSocket streaming
• Idempotent writes, outbox → Redis Streams fan-out, retries, DLQ
• Observability first: route histograms, cache hit ratio, stream lag; OTel traces across API → DB → Redis → worker
• /chat orchestration with tool calls and RAG; switch to local models by setting OPENAI_API_BASE
• CI/CD with lint/type/tests + ownership guards; small PRs, fast merges

Why it matters: you can see p50/p95 numbers, traces, and the exact code that keeps releases predictable.

• Creative Coach (FastAPI + Celery): streaming insights over SSE/WebSockets; multi-model routing with OpenAI-compatible clients cut serving cost by ~98%; tracked 50+ experiment metrics in MLflow with Prometheus/Grafana dashboards.
• AI Agent Development System: multi-agent dev platform with real-time telemetry, eval gates, prompt governance, canary + rollback, and local-model support (Ollama / llama.cpp / vLLM). Saves ~13.5–31 h/week in my workflow and improves DORA-style outcomes.
• RAGline (public PoC): streaming-first Python backend with JWT multi-tenancy, idempotent writes, outbox → Redis Streams, and /chat orchestration with RAG (Qdrant/pgvector). Local model switch via OPENAI_API_BASE; first-token and p95 latency measured with OpenTelemetry.

Python: FastAPI, AsyncIO, Pydantic v2, SQLAlchemy 2.0, Celery
Data/Infra: PostgreSQL, Redis, Docker, GitHub Actions
AI/LLM: OpenAI API, RAG with Qdrant/pgvector, MLflow; local models via Ollama / llama.cpp / vLLM
Observability: Prometheus, Grafana, OpenTelemetry, Sentry
Mobile: Flutter, Dart (BLoC/Cubit, Provider, auto_route), Android/Kotlin (BLE)
Cloud: AWS (Lambda, AppSync, DynamoDB), GCP (GKE/Cloud Run), Firebase

• Contract-first: OpenAPI & event schemas before code
• Metrics-first: set SLOs, watch p95, fix root causes, add a safeguard
• CI discipline: tests, lint, types, security checks on every PR
• Small batches: feature flags, frequent releases, clean rollbacks
• Leadership: led 3 teams (2–6 devs); set review/testing standards; mentor kindly, ship steadily

• Email: serbyn.vitalii@gmail.com
• LinkedIn: https://www.linkedin.com/in/vitalii-serbyn-b517a083/
• Portfolio: https://serbyn.pro