CTO · Head of Engineering · Engineering Manager · AI Product Builder

Yury Kononov

For 15 years I've been building engineering organizations, scaling high-load platforms, and adopting AI in software development. I help engineers grow into CTOs and companies ship faster.

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Yury Kononov — CTO, Head of Engineering, Engineering Manager, AI Product Builder
fig. 01 — CTO
15
years of technology leadership
200+
specialists managed
99.999%
SLA on platforms serving millions
50+
projects delivered
01 — EXPERTISE

Areas of my responsibility

What I actually do as a CTO — and the metrics I measure it by.

Strategy & Architecture

  • Technology strategy and roadmap tied to business goals
  • Company-level architecture: microservices, high-load, integrations
  • Build vs buy, tech radar, platform and vendor choices
  • Technology risk and security
METRICS
SLA / uptime · ROI of tech investments · run vs change · tech-debt share

Delivery & Processes

  • SDLC: planning, prioritization, predictable releases
  • Engineering practices: CI/CD, tests, code review, documentation
  • Incident management and delivery quality
  • Cross-team dependencies and tech-debt management
METRICS
Time-to-market · lead time · deployment frequency · change failure rate · MTTR

People & Organization

  • Engineering org structure: teams, roles, areas of ownership
  • Hiring from developers to EMs and architects
  • Growth and retention: career tracks, performance reviews, mentoring
  • Onboarding and ramp-up of new engineers
METRICS
Attrition · eNPS · time-to-hire · time-to-productivity

Engineering Economics & AI

  • Engineering budget: FTE, infrastructure, vendors
  • Optimizing release costs and team unit economics
  • AI in the SDLC: assistants, code generation, review
  • Measuring AI impact and scaling practices across teams
METRICS
Release & FTE cost · cost per feature · velocity gain from AI
THE SDLC PROCESS I BUILD
Click any stage to see the owners, key metrics, and level of automation.

Stage 01: Idea

Owners
Product Owner, CPO, Business
Metrics & artefacts
ROI, Time-to-Market, alignment with company goals
Tools & AI
Jira Product Discovery, AI hypothesis generation, Miro
Every stage runs on transparent reporting for the business, CI/CD, automated tests, and AI assistants.
02 — OUTCOMES

What you get

FOR AN EMPLOYER
Cut time-to-market by optimizing the software production process.
Build teams and processes from scratch.
Establish SDLC processes.
Manage the backlog properly, including architectural and technical debt.
Set up onboarding and employee development.
Adopt AI in development with measurable impact.
Reduce release costs.
Audit the architecture and prepare the platform for load growth.
Migrate off legacy/monolith seamlessly, with zero downtime.
Build transparent engineering metrics the business understands.
Deliver a project end-to-end: from idea to production support and growth.
IN CONSULTING & MENTORING
A concrete action plan for your problem after the very first session.
Growth to your next role: senior → team lead → EM → CTO.
An architecture review: risks, priorities, what to fix first.
A second opinion from an experienced CTO on hard decisions.
Interview prep for leadership roles: from resume to offer.
A personal growth plan with regular sessions and feedback.
Management skills: delegation, 1:1s, hiring, handling conflict.
Hands-on use of AI tools in your day-to-day work.
Real cases from FinTech and e-commerce — not theory.
CLIENTS & BRANDS
03 — RESUME

Experience

2023 — present
CTO · Major Russian bank
  • Technology strategy for 7 fintech products: 20 engineering teams, 200+ specialists.
  • Replaced a mission-critical banking platform in 1.5 years — 60+ microservices with a 99.999% SLA for millions of clients.
  • Cut time-to-market from 2 months to 2 weeks and release costs by 40%; rolled out AI assistants across 15 teams.
2021 — 2023
Engineering Manager · Major Russian bank
  • Launched a product from scratch: team of 10, MVP in 3 months, scaled into a retail online bank with 1M+ users.
  • Omnichannel microservices platform with 40+ integrations and a 99.999% SLA across 3 lending business lines.
2020 — 2021
CTO · Aero eCommerce Agency
  • High-load e-commerce platform from scratch in 6 months: 20+ services, 50K+ orders per day, 5K+ concurrent users.
  • Zero-downtime traffic migration from a monolith to microservices.
2018 — 2020
Head of Backend · Aero eCommerce Agency
  • Led the backend practice: 25 developers and 3 DevOps; migrated solutions from 1C-Bitrix to microservices and Kubernetes with CI/CD.
2016 — 2018
Team Lead · Aero eCommerce Agency
  • Flagship e-commerce product in 5 countries, ~40 integrations, stable at 2M visits per month.
EDUCATION
P. G. Demidov Yaroslavl State University — Applied Informatics in Economics, 2007–2012.
LANGUAGES
Russian — native · English — B2. I work with teams and clients in both languages.
04 — SERVICES

Mentoring & Consulting

Three formats — from a single session to long-term consulting for teams and companies.

/01

One-off consultation

60–90 minutes on a specific question: architecture, high-load, development processes, career, AI tooling.

Book a slot
/02

Mentoring

Ongoing work with team leads, EMs, CTOs, developers, analysts, QA, and AI engineers. A personal growth plan.

Get in touch
/03

Company consulting

Architecture and SDLC audits, AI adoption in development, building and scaling engineering organizations.

Request a call
05 — PROJECTS

Courses, plugins, and other work

Open source and open content — the things I ship outside of a full-time role.

Course · Free · CC BY 4.0 · English and Russian

AI-PDLC: how AI changes the way products get built

The focus shifts from writing code to the specification and the environment around the model. Core idea: outcomes are decided by the harness around the model, not by the choice of model. Built around a single worked example — no technical background required.

9 modules52 minutesquiz after each moduleglossary
Take the course
  1. What AI-PDLC is
  2. How it differs from the usual process
  3. Why "we bought an assistant" did not work
  4. What the research says
  5. Where to put the money
  6. Control, security, the regulator
  7. What happens to people
  8. How to measure the impact
  9. Where to start
Plugin · Claude Code · Open source · English

Brownspec: spec-driven for brownfield codebases

The same three artifacts as any spec kit — requirements, design, tasks — but for a codebase that already exists. Conventions are read out of your code instead of declared. The design carries the blast radius of the change, with provenance. The name is the whole thesis: brownfield, not greenfield. Regulatory profiles (152-ФЗ; GDPR next) are optional and off by default.

Claude Code pluginspec / design / tasksblast radiusopt-in policies 
/plugin marketplace add yknnv/brownspec
/plugin install brownspec@brownspec
  • /brownspec:init — samples your code and writes down conventions; policies stay yours
  • /brownspec:spec — six-question interview → spec.md and the policies it touches
  • /brownspec:design — blast radius, contracts, rollback → design.md
  • /brownspec:tasks — ordered, deployable at every step → tasks.md
  • Interviews resume where they stopped; regulatory profiles apply only when you opt in
Passion project · Printable book · MIT + CC BY-NC-SA

DIVE logbook: A5 dive log, print-ready

Outside of work I dive — recreational, licensed. I could not find a paper A5 log I liked, so I made one for myself and my dive buddies and put it in the open. 130 pages, 42 record spreads, a reference section (hand signals, lost buddy, DCS, emergency contacts, gas planning). Field labels bilingual RU/EN.

130 pages42 record spreadsA5 monochromefree PDF
What is inside
  • Dive records: profile, gas, gear, self-assessment — one spread per dive
  • Reference: BWRAF, ascent, lost buddy, DCS, emergency contacts, gas planning, RMV, nitrox & MOD, no-fly limits
  • Format: A5 148 × 210 mm, ring-binder punch, monochrome — any print shop, any office printer
  • Layout: Python / reportlab, one-command build; edits are text, not InDesign
Passion project · Printable book · MIT + CC BY-NC-SA

SAIL logbook: A5 sailing log, print-ready

I also sail — licensed skipper. Same story as the dive log: nothing on the market was a clean fit, so I built a paper A5 log for myself and my crew and shared it. 144 pages, 42 passage spreads, a sailor's reference (MOB, MAYDAY, COLREGS, knots, tides, IALA buoys) and an emergency card.

144 pages42 passage spreadsA5 monochromefree PDF
What is inside
  • Passage records: route, headings, wind, crew, sails, engine, notes — one spread per passage
  • Reference: MOB, MAYDAY, points of sail, COLREGS, lights & day shapes, knots, tides, IALA buoys, charter handover
  • Format: A5 148 × 210 mm, ring-binder punch, monochrome — cheap at any print shop
  • Layout: Python / reportlab, one-command build; edits are text, not InDesign
06 — FAQ

Frequently asked questions

How I run the first session
60–90 minutes online. Before we meet, you send a short description of the problem. In the session we go through context, risks, priorities — and you leave with a concrete action plan.
Who I work with
Developers, team leads, engineering managers, CTOs and product tech leads, plus analysts, QA and AI engineers who want to grow on leadership or senior technical tracks.
Whether I work with international companies
Yes. I run mentoring and consulting fully remote, worldwide. Sessions in English (B2) and Russian.
How I measure the impact of AI in the SDLC
With a small metrics set: lead time, release cost, cost per feature, share of AI-assisted work, MTTR and change failure rate. Before/after measurement is a mandatory part of every rollout.
How I handle NDAs and invoice-based payment
For company consulting it's the standard flow: NDA, contract, invoice-based payment. Private mentoring is more flexible, including per-session payment.
Which stacks and industries I know best
FinTech (banking platforms, lending products, integrations) and e-commerce (high-load, catalog, checkout, logistics). Stack: JVM, Go, TypeScript, Python, Kafka, PostgreSQL, Kubernetes, DevOps practices.