AI Systems Engineer LLM Agent Architect Astana · Kazakhstan

Aleksandr Tikhonov.

I design production systems around LLMs: RAG services, agent orchestration, semantic search, and self-hosted inference — under strict latency and cost constraints.

Open resume → View systems GitHub
2.5+
years of production AI
20
universities in pilot
7500+
Protocore tests

Reference architecture

FIG. 1 · GENERAL VIEW
chat / web clientsSSE streaming · RU/ENin prod: LanGPT · Protocore Chat
admin dashboardcontrol plane · RBACin prod: Protocore Dashboard
API · agent orchestrationFastAPI · contracts · quality gatesin prod: LanGPT API · Protocore Runtime
searchBM25 + kNN + RRFin prod: Search 2.0 · AI-Snippets
LLM inferencevLLM · load balancerin prod: LanGPT · AI-Snippets
sandboxgVisor · k8s podsin prod: Protocore Sandbox
infrastructureKubernetes · Helm · Prometheus · RabbitMQ · Redis · Postgresin prod: EBS Lan · Protocore
THE STACK I WORK WITH, DRAWN AS A SYSTEM · HOVER A NODE — WHERE IT RUNS IN PROD

A diagram beats a bullet list. This exact scheme runs end-to-end in my self-hosted platform Protocore.

Flagship SYS—01 BETA
Lead project · ascorblack-labs

Protocore.

A protocol-first runtime for stateful, tool-using AI agents: strict core contracts, durable run state, observability and real-time SSE streaming. It deploys entirely on your own infrastructure and ships with a reproducible-research publication site. Flagship project — founder / solo.

  • Protocol-first core with enforced boundaries
  • Durable, stateful agent runs
  • Observability · real-time SSE streaming
  • Reproducible-research publication site
  • 7,400+ automated tests · reproducible benchmarks
Open the platform → About the project

Systems index

7 entries · 2024—2026
SYS—02
PROD

LanGPT

RAG/LLM assistant for academic tasks on the EBS Lan (Russian academic e-library platform). Public API with JWT and SSE, RAG over Elasticsearch, LLM backend balancer, moderation.

20 universities in pilot
37,000+ requests
6,500 users
→ SYS—03
PROD

AI-Snippets

Snippet answers over EBS Lan search results: cache, BM25+RRF, quality gate, circuit breaker, graceful degradation. Stress test: 13.2 hours without a single infrastructure error.

55,200 requests · 0 × 5xx
median 4.6 s / SLA 5 s
→ SYS—04
PROD

Search 2.0

EBS Lan search platform: hybrid BM25 and vector search, subject-area classifier, spell checker, video search. All ML components are CPU-only.

hybrid BM25 + kNN + RRF
ML: CPU-only
→ SYS—05
R&D

swiftclf-tuna

Bilingual (RU/EN) hierarchical intent classification for routing AI assistants. AI-orchestrated research: selective prediction, calibration, CPU-first deployment.

accuracy 87.07% · ECE 0.061
p95 77 ms / 1 CPU
→ SYS—06
OSS

AI Audit Kit

A portable .audit standard: turns AI code audit, triage, and bulk fixes into a reproducible pipeline with artifacts instead of chat sessions.

audit → triage → fix → review
open source
→ SYS—07
OSS

Agent Tooling

Tools for autonomous agents: notify-telegram-cli — dependency-free notifications, kb-genesis — knowledge bases for agent environments, LightUniLLM — a lightweight LLM framework.

notify-cli · kb-genesis · LightUniLLM
github.com/ascorblack
→

Profile

Since January 2024 — AI Systems Engineer at EBS Lan (Russian academic e-library platform): I own the backend and infrastructure side of the company's key AI services, from architecture to production support in Kubernetes.

In parallel — founder of ascorblack-labs: Protocore, a self-hosted AI agent platform, running entirely on my own infrastructure (GitLab, Harbor, k3s).

My strength is engineering production systems around LLMs rather than generic model training: reliability, observability, and degradation that never surfaces as an error.

Specification

  • LLM / RAG / agent orchestrationcore
  • Python · FastAPI · asynciocore
  • Elasticsearch · BM25 + kNN + RRFcore
  • vLLM · local inferencecore
  • Kubernetes · Helm · gVisorops
  • RabbitMQ · Redis · PostgreSQLdata
  • Prometheus · Grafana · OTLPobs
  • Next.js · React — platform frontendsui

Writing

1 document(s)
DOC—01

"AI Audit Kit: a reproducible standard for AI auditing of large codebases"

A formalized view of AI Audit Kit as a portable standard operating framework for auditing, triage, automated bug fixing, and an independent review loop.

→