Grigoriy Dobryakov — Head of AI Engineering

AI-Native Engineering Leadership

Connect as MCP Contact
Head of AI Engineering

Grigoriy Dobryakov

I come into mature organizations, fix what's broken, and turn engineering into a platform the business grows on — new revenue the old setup couldn't reach. At a 9,000-person enterprise I took AI from a first proposal to adoption top-down across the business — logistics, production, retail, finance — until it ran as a standing department with its own head. The approach embeds and sticks well beyond my own hands.

I build the systems that make this work — out of people and agents, where AI absorbs the rote layer and people operate at judgment. Coding with AI is table stakes; the scarce skill is making the delivery and management layer AI-native, where margins are won or lost. I design and run these systems as a hands-on engineer.

Languages: English (professional working), Russian (native)

Connect on LinkedIn Cases Courses
Engineers on the AI-native SDLC I built
~200
Systems architected
40+
User profiles on the personalization platform
199M+
From first AI proposal to a standing AI department
6–8 mo

Systems I build

Proof in two directions: two AI systems I designed and run solo, and one AI practice I scaled across a 9,000-person enterprise. I build these systems hands-on, and I make them embed and stick across large teams and business functions.

AI-native delivery intelligence

Built solo · proven on live presales

The system holds the full state of an engagement from the first pre-sales call: every call, email, document and commitment, captured across channels and reconciled. It weighs each fact by who is authorized to state it, keeps provenance on every claim, and flags contradictions, unkept promises and aging questions before they cost margin. On live enterprise presales it caught a headcount gap, a dropped discount and an SLA mismatch before signature.

ai-delivery.dobryakov.net →

Next Move Engine

Public · self-demonstrating

An AI system that runs market intelligence and content operations end to end: reads signals, decides the next move, ships. Same engineering discipline, in the open — a live demonstration of the systems I build.

nextmoveengine.com →

Org-wide AI adoption at Askona

9,000-person enterprise · adopted and stuck

I took AI from a first proposal to a dedicated department with its own head in ~6–8 months, pulled top-down through the business rather than pushed at engineers. It spread across most business functions beyond IT — logistics, production, retail, 1C, finance — with even non-technical staff building their own tools. Proof these approaches embed and stick across large teams and processes, and outlast the person who started them.

Case study →

And beyond that — a multi-model video pipeline (character, scenes, TTS, whisper-timed keyframes, image-to-video, camera motion) and a production-grade agent/MCP toolchain — all of it built and running in production.

Cases

Metrics and business outcomes. The full write-up is on each case page.

Askona — AI leadership and enterprise architecture

$680M-revenue retailer · 20+ teams · 40+ systems · 15M customers

Event-driven architecture, AI in production and team enablement on top of a multi-party integration layer.

Large system integrator — turnaround

1500+ employees · Enterprise wins · Predictable delivery restored

Restored delivery predictability in an underperforming B2B department, stepped up to enterprise accounts like Askona.

UMI.CMS/UMI.RU — from boxed product to SaaS

Revenue +50% · 65k → 1M+ users · Weekly releases · 70+ test envs

Engineering-function turnaround and product ownership through the move to a cloud model.

PersonaClick — ML at scale

199M+ profiles · ML in production · Cashflow + retention

ML and predictive analytics on 199M+ profiles, opening large-retailer accounts that were out of reach.

External mentions

Sergey Pimenov · Facebook, May 2026

"A conversation with Grigoriy Dobryakov on building working AI setups from vibe-coding, agent pipelines, and AI tools"

Recommendations

From colleagues and managers on my LinkedIn profile.

"Grigoriy consistently demonstrated exceptional technical leadership and deep expertise as an Enterprise Architect. He was one of the key drivers behind the adoption of event-driven architecture and microservices across Askona's IT landscape — pragmatic, collaborative, and always focused on the right solution for both business and technology."

Aleksander Zinger · Director-level IT Business Partner, Askona

"I highly recommend him as an exceptionally competent professional in Enterprise Architecture, Solution Architecture, AI-driven innovation, and technical leadership. He led the company's strategic AI initiative, driving the adoption of AI tools and practices across the organization."

Ilya Chernov · Head of PMO IT, Askona

"He showed himself as an excellent technical leader. He put the development process to a new level, and the best practices he taught the team significantly improved the quality of the product."

Anton Prusov · CTO at Varwin — reported to Grigoriy

Engineering breakdowns

How it actually works

All breakdowns →

01 · Facebook · 105k views

Book-as-context: architecture grounded on Tanenbaum

Ask for "ERP integration" → agent outputs FTP. The root isn't the model — it's missing domain context.

04 · GitHub · ★8 / 4 forks

Async MCP server with job queue

A naive MCP server blocks on long operations. Submit → poll, and 404-after-completion as a deliberate trade-off.

09 · YouTube · 3,600 views

Sending to Kafka is a two-phase commit

"Fire and forget" — wrong mental model. Dual write without outbox is a deferred incident.

07 · Governance

Cursor rules as governance

When 10 people vibe-code, standards drift. A guardrail in the toolchain, not a quarterly lecture.

Courses

Courses

AI-Driven Development Lifecycle

The development lifecycle handed to AI phase by phase — to the limit where there's no human anywhere, including the project manager. Running case — the SaaS "Vitrina".

Full course →

Enterprise AI Governance Architecture

Governance as a control plane between the business and the models, not a policy PDF: a stack of 10 control planes. Running case — the bank assistant Kovcheg.

Full course →

AI Compliance: EU AI Act & the Global Regime

How to read the AI regulatory regime and live in it: flagship EU AI Act, plus a map of the US, UK and China. Running case — HR-tech SaaS Kompas.

Full course →
Live MCP server dobryakov-expert

Query my profile as an MCP server

I run a production MCP server that represents my professional profile. Connect it to your Claude and query my experience directly — a live demonstration of the systems I build.

Claude Codex Cursor Streamable HTTP 7 tools
Connect See what's inside
.mcp.json

https://mcp.dobryakov.net/mcp 

"dobryakov-expert": {
  "type": "http",
  "url": "https://mcp.dobryakov.net/mcp"
}

Who This Is For

Find your role — and write to me if it fits.

CEO / Founder

I take ownership of the whole engineering function (delivery + economics) and make AI the operating model, not a set of pilots — freeing your time for strategy.

CTO / VP Engineering

One owner for people, AI architecture and delivery instead of two roles; single accountability for the production outcome.

Head of AI / VP AI / CAIO

I translate the AI roadmap into engineering practice: initiatives reach production and outlast the rollout, instead of dying as pilots.

Chief Digital Officer

I align business goals with engineering execution and land the digitalization portfolio as predictable outcomes.

HR / Head of Recruitment

I fill a combined role (engineering leadership + AI systems expertise) with one profile; a candidate easy to defend to CTO and CEO at once.

Chief Architect

I embed AI practices without breaking the foundation or creating unmanageable AI tech debt.

Expertise

AI as an operating model — a repeatable operating layer across three dimensions, not one-off prompts. Stack: Anthropic Claude / MCP, RAG & agentic, Kafka / event-driven, AWS, ClickHouse / ELK, CI/CD — designed and driven. First 90 days — building the system: wiring people and agents into one model with explicit owners, metrics and stop criteria.

AI in the SDLC

A repeatable operating layer: AI-assisted coding as team practice, AI in testing and review, eval as a release criterion, observability of model decisions.

ADLC course → · Engineering breakdowns →

AI governance

GDPR / EU AI Act controls built into the system with working proof: human-in-the-loop, egress redaction of PII, provenance and eval at release. Engineering-side conformance, not a legal role.

AI Governance course → · Checklist → · Egress gate →

AI in product and operations

Production-ready RAG (not "PDF into a vector DB"), event-driven AI (LLM as a stream consumer), agentic pipelines for real enterprise constraints. Live proof — Next Move Engine.

Next Move Engine → · Async MCP server →

Materials

A single hub with all public assets: blogs, videos, courses, community, project, and profiles.

Blogs and Articles

Practical writing on project management, engineering leadership, software delivery, and applied AI in production contexts.

Videos and Courses

Public video materials on engineering leadership, architecture, and practical AI operations.

Connect

The clearest picture of how I think is in the cases, the engineering breakdowns, and Next Move Engine, my own AI system running the same patterns I ship for others. Open work is on GitHub.

Open to

Head of AI Engineering / Head of AI Platform / AI Architect, or AI-augmented delivery leadership — full-time, fully remote. Based in Serbia / Montenegro, CET/CEST — full EU-hours overlap. Montenegro work permit (renewable); or via EOR / contractor.

Connect on LinkedIn Open to roles — let's talk