# Vladimir Stryy

AI Infrastructure / Platform Lead — Lead SRE background
Homepage: https://xeonerix.xyz/
GitHub: https://github.com/XeoneriX
Telegram: https://t.me/xeonerix

## Positioning

Infrastructure / Platform Lead who operates private AI / GPU inference as
production infrastructure, on top of a deep SRE base: private cloud, distributed
storage, highload traffic platforms, DDoS resilience, observability and network
telemetry. The differentiator is the fusion — an infrastructure engineer who
runs LLM / RAG inference as an operated platform used by real engineering teams.

Best-fit roles:

- AI Infrastructure / GPU Infrastructure Lead
- Inference / AI Platform Lead
- Head of Infrastructure
- Principal / Staff SRE (storage / network / AI-infra heavy)
- Infrastructure / Platform Engineering Lead

## Summary

8 years of experience in SRE and infrastructure engineering. Strongest
areas: Ceph and private cloud, highload infrastructure, P0/P1 incident response,
multi-tenant observability, BGP/BMP telemetry, IaC, platform migrations and
on-prem LLM/RAG systems for SRE workflows.

## Core skills

- AI infrastructure: private LLM serving, GPU inference, vLLM, llama.cpp, LiteLLM routing, Qdrant, bge-m3, reranking, RAG, Langfuse
- Private cloud: Proxmox, OpenStack, VMware, Ceph, ZFS
- IaC: OpenTofu, Terraform, Packer, Ansible, GitLab CI
- Observability: VictoriaMetrics, VictoriaLogs, ClickHouse, Grafana, Vector
- Network: BGP, BMP, SRv6, SR Policy, EVPN, FlowSpec, Juniper, Huawei
- Security and access: Vault/OpenBao, RADIUS, Wazuh, audit trails
- Programming: Go, Python, Bash, FastAPI, Flask, Celery
- Datastores and messaging: PostgreSQL, Redis, NATS JetStream, ClickHouse

## Experience

### Lead SRE / Infrastructure Lead

Cybersecurity / highload infrastructure platform, 2024–present

- Lead infrastructure direction for a highload platform with carrier-scale
  L3/L4/L7 traffic and DDoS-mitigation workloads.
- Build and operate private-cloud infrastructure based on Proxmox, Ceph,
  OpenTofu, Packer, Ansible and GitLab CI.
- Drive VM fleet migration from ESXi/vSAN-style infrastructure to
  Proxmox/Ceph-style infrastructure with zero customer-visible downtime.
- Design multi-tenant observability using VictoriaMetrics, VictoriaLogs,
  ClickHouse, Vector and Grafana HA.
- Build network telemetry around BMP/BGP signals, including SRv6, SR Policy,
  EVPN and FlowSpec.
- Own architecture, migration planning, cost models and operational standards.
- Provide technical leadership for a core team (2 direct reports) and set
  cross-team technical direction for ~5–7 network and adjacent infrastructure
  engineers by influence.
- Build and operate a private on-prem LLM/RAG inference platform — LiteLLM
  routing, vLLM + llama.cpp GPU serving, Qdrant + bge-m3 retrieval with
  reranking, Langfuse tracing — for SRE automation, incident response and
  merge-request review; adopted across multiple engineering teams, with first
  external adoption via an IDE coding-assistant (Cline) integration.

### Senior SRE / Core Infrastructure SWAT

Large-scale consumer-internet / highload platform, 2022–2024

- Worked in a core SRE/SWAT team handling critical infrastructure incidents and
  production degradations.
- Acted as escalation point for P0/P1 issues across storage, compute and
  platform infrastructure.
- Stabilized Ceph clusters at ~100+ server and ~10 PB raw scale: disk failures,
  slow ops, recovery storms, rebalance and performance recovery.
- Migrated Ceph services from Kubernetes-style placement to bare-metal-style
  operation where that reduced operational complexity and improved reliability.
- Contributed to Python services, CI/CD, PostgreSQL, Keycloak, Docker Swarm,
  object storage, performance tooling and observability.

### Senior Cloud Infrastructure Engineer

International CDN / cloud provider, 2020–2022

- Operated and developed distributed IaaS infrastructure based on OpenStack,
  Ceph and S3-compatible storage.
- Supported 5 availability zones and ~80+ compute/storage servers.
- Maintained SLA for 50–80 B2B customers including enterprise customers.
- Built resource accounting and billing pipelines for VM, storage, IP and S3
  usage with PostgreSQL integration.
- Solved inconsistent S3 usage-data accounting with log-analysis pipelines.
- Worked on Ceph expansion, balancing, recovery, Ansible bootstrap, capacity
  planning, hardware selection, customer migrations and troubleshooting.
- Implemented SSO integration with a POST-based flow.

### Systems Engineer / Python Developer

GDS / travel-tech, 2018–2020

- Designed and implemented passenger-data processing pipelines replacing legacy
  shell workflows.
- Built SFTP ingestion, normalization, validation, retry and delivery-control
  logic for external systems.
- Implemented audit trail for data processing and delivery status.
- Built backend services with Python, Flask, Celery and PostgreSQL; tuned
  PostgreSQL queries and indexes.
- Built Asterisk/LDAP telephony integration and automation bots.

## Projects

### BGPeek

Open-source BGP looking glass for ISP / IX operators.

- GitHub: https://github.com/XeoneriX/bgpeek — Apache-2.0
- Stack: FastAPI, Jinja2, HTMX, Tailwind, PostgreSQL (asyncpg), Redis, Netmiko, Docker Compose
- Multi-vendor SSH: Juniper JunOS, Cisco IOS/XE/XR, Arista EOS, Huawei VRP; RPKI validation; parallel cross-device queries with diff
- Auth: API key, bcrypt, LDAP, OIDC (Keycloak); RBAC (admin / NOC / public) with per-role output filtering and device-level restrictions
- Security: Fernet-encrypted SSH credentials, Redis rate limiting with circuit breaker, HMAC-SHA256 webhooks, non-root containers
- Ops: Prometheus /metrics, PostgreSQL audit log, health checks, UUID permalinks

### Private AI / SRE inference platform

- Operate a private LLM/RAG inference platform: LiteLLM OpenAI-compatible
  routing, vLLM + llama.cpp GPU serving of Qwen-family models, Qdrant + bge-m3
  retrieval with cross-encoder reranking, Langfuse tracing.
- Treat private inference as an infrastructure workload: NUMA-aware placement,
  hugepages, CPU/memory isolation, concurrency limits, context-budget sizing.
- Benchmarks for CPU-only inference isolation, GPU serving, prefix-cache
  behaviour and production co-tenancy impact.

## Education

Moscow State Technical University of Civil Aviation
Information security of telecommunication systems