My Tools

I run prod and dev clusters, wire up CI/CD, and spin up ephemeral environments for team workflows. In production I lean on operators, networking, and security policies until delivery stays predictable.

I containerize services, build images, and ship them to Kubernetes or dedicated environments. The context is enterprise microservices and repeatable setups from dev through prod.

I deploy from internal and community charts and maintain charts for our microservices. The emphasis is reusable values, environment parity, and consistent releases.

I work with compute and managed services in the corporate cloud: administration, service configuration, and project rollout under load and security requirements.

I explore Sber Cloud on personal projects—APIs, networking, and platform services outside production-critical paths.

I prepare and run servers for enterprise systems and infra services: baseline hardening, services, and routine automation.

I build pipelines for build, test, and deploy of microservices, wire in test stands, and align stages with engineering teams.

I automate personal and small projects—checks, builds, publishing artifacts and static sites without a heavy self-hosted runner footprint.

I design schemas and migrations for services and data marts, tune queries and indexes in enterprise environments. The outcome is a coherent model and predictable performance.

I use it for cache, queues, and short-lived state next to services. I tune memory policies and observability so bottlenecks show up early.

I design event exchange between services—topics, message shapes, idempotency, and failure modes. Context is distributed enterprise systems.

I set up metrics and dashboards for enterprise systems and tie alerts to real SLOs. I also explore eBPF as a path to observability with fewer agents.

I work on logging standards, views, and adoption across teams. The goal is unified search across services and faster incident triage.

I integrate the knowledge base with search and agents—export, indexing, and freshness in RAG-style flows.

I orchestrate ETL/ELT and scheduled jobs for data and ML pipelines. DAGs stay readable and reproducible across environments.

I build ingestion → chunking → vector retrieval → context ranking for enterprise content. The practical focus is answer quality and measurable metrics, not demos alone.

I choose and run vector stores for load and data policy—collections, filters, and hybrid search where it matters.

I run local models for prototypes and private scenarios without shipping data out. I trade off quality and inference cost on available hardware.

I run a unified LLM web UI for experiments and demos—roles, prompts, and local or remote providers.

I stitch AI and integration workflows with humans in the loop—triggers, branching, and resilience to flaky external APIs.

I explore the tool in a safe sandbox—automation scenarios and boundaries for what an agent may do.

I pull models and datasets for experiments and fine-tuning; I watch licenses and constraints on enterprise data.

I test the corporate coding assistant on personal projects—completion, refactors, and prompt hygiene.

I try the Sber Git platform for repos, CI, and collaboration away from the main toolchain.

I study cloud dev environments—startup time, extensions, and flows for distributed teams and fast reviews.

I pilot Yandex CodePlatform inside corporate workflows—repos, reviews, and handoff to existing CI/CD.

I build backends and microservices, review code, and align team practices. Daily work: gRPC/HTTP, concurrency, profiling, and clear module boundaries.

I write scripts, PoCs, and utilities for AI, data, and automation—fast iterations and careful packaging when a prototype matures.

I design storage for services and reporting, tune joins and aggregates. The result is schemas that survive data growth and feature churn.

I design HTTP APIs for internal and external integrations—versions, errors, contracts, and alignment with consumers.

I deliver projects on the portal and CRM—new business-facing capabilities and integration architecture with neighboring enterprise systems.

I shape the enterprise IS landscape—integrations, capability growth, and business-process alignment at an architectural level, not one-off patches.

I set standards for describing systems—context, containers, components, and relationships—so teams and stakeholders share one language.

I produce diagrams for decisions and reviews—quick sketches for discussion and clean artifacts for the knowledge base.

I configure spaces, knowledge practices, and links between work items and artifacts; I automate routine so project state is not trapped in chat.

I introduce rituals and flow transparency, facilitate ceremonies, and align business expectations with engineering reality.

I build static sites with minimal client JS; this project is my practical playground for speed and DX.

Primary editor for Go and Python; in Cursor I use AI deliberately—diffs and tests still stay human-owned.

I build bots for corporate notifications and personal experiments—commands, webhooks, and resilience to traffic spikes.