Platform & AI Infrastructure Engineer
Experience
4 years 5 months
Profile
Platform / SRE engineer with deep expertise in automation, observability and infrastructure for ML workloads. Specialized in high-load local model inference (vLLM/Triton), GPU utilization optimization and event-driven RAG systems. Product-minded: I do not only configure infrastructure, but also build internal automation tools, CLI utilities and lightweight client interfaces for monitoring and operating systems end-to-end.
Tech Stack
Platform & DevOps
Backend & Observability
Internal Tooling & Client Interfaces
Professional Experience
RTI JSC
Platform / DevOps Engineer
- Designed and deployed high-load LLM inference (Qwen) using vLLM and Ollama, optimized GPU utilization with CUDA and reached 150+ tokens/sec on a single GPU.
- Implemented a scalable RAG pipeline with vector search (pgvector, Qdrant) and event-driven orchestration based on n8n and Supabase.
- Deployed and configured a ClickHouse cluster from scratch for infrastructure metrics collection.
- Automated ML model delivery into Kubernetes (k3s) and configured GPU metrics monitoring with Prometheus and Grafana.
- Built internal cross-platform client applications for IoT telemetry monitoring and remote diagnostics of microcomputers.
Tech Stack: vLLM, Ollama, Qwen, CUDA, Kubernetes/k3s, Docker, Prometheus, Grafana, ClickHouse, pgvector, Qdrant, n8n, Supabase, Python/FastAPI, Kotlin, Flutter
Sber
DevOps / Automation Engineer
- Developed load-testing tooling: custom CLI wrappers and traffic generators in Go/Python.
- Improved system stability under load with Chaos Engineering and stress tests (k6, wrk).
- Created isolated Docker-based testing environments with emulation of heavy external services (Spark/Hadoop).
- Reworked observability by adding business metrics and long-term storage in VictoriaMetrics and ClickHouse.
Tech Stack: Go, Python, k6, wrk, Chaos Engineering, Docker, Ansible, Prometheus, VictoriaMetrics, Grafana, ClickHouse, ELK Stack, Spark, Hadoop
Rosseti Digital JSC
DevOps / Data Platform Engineer
- Designed and operated a fault-tolerant process automation platform based on Apache Airflow and optimized DAG structure.
- Implemented centralized logging and tracing on ELK Stack, reducing mean time to recovery (MTTR).
- Containerized system components and standardized dev/prod environments with Docker.
- Administered distributed data stores (PostgreSQL, MariaDB, MinIO/S3) and tuned their performance.
Tech Stack: Python, Apache Airflow, PostgreSQL, MariaDB, MinIO/S3, Docker, ELK Stack, Grafana, Linux
Franklins Burger
Fullstack Developer / Automation Engineer
- Built automated software update delivery infrastructure for endpoints in Yandex Cloud from scratch using Docker and Ansible.
- Designed and implemented a distributed data bus for centralized point configuration and advertising management.
- Developed server-side POS integration modules (IIKO) in C#/.NET and Python (FastAPI).
- Designed and launched internal web tools for business automation, CRM systems and HR bots.
Tech Stack: Python/FastAPI, C#, .NET, PostgreSQL, Docker, Ansible, Yandex Cloud, IIKO SDK, JavaScript, Vue.js, Svelte
Platform / MLOps Projects
High-Performance ML Inference Serving
vLLM/Ollama inference platform
- Local LLM deployment stand for Qwen/Llama based on vLLM/Ollama with production-like containerization.
- Configured autoscaling, throughput/latency controls and CUDA core monitoring with Prometheus and Grafana.
- Prepared Python tooling for smoke tests, health checks and operational inference diagnostics.
Tech Stack: vLLM, Docker, CUDA, Prometheus, Grafana, Python
AI-Driven Enterprise Automation Core
Agent workflows and RAG automation
- Orchestration stack for agent workflows integrated with vector databases for contextual search (RAG).
- Connected n8n workflows, FastAPI services and Supabase into an event-driven enterprise automation core.
- Added vector storage, retrieval and context control for internal knowledge workflows.
Tech Stack: n8n, pgvector, Qdrant, FastAPI, Supabase
Multi-Platform CI/CD Automation Framework
Universal delivery automation template
- Universal software delivery automation template for shorter release cycles and stable builds.
- Includes containerization, automatic artifact signing, release notes generation and crash reporting integration.
- Supports reproducible environments, quality gates and fast rollback paths for internal products.
Tech Stack: GitLab CI, Fastlane, Docker, Ansible, Gradle