Grigoriy Dobryakov

CEO / Founder

When you need an engineering organization where AI is the operating model — not one department's experiment — I take ownership of the whole function and free your time for strategy. An agent is a multiplier of your organization, not its competitor: without an owner who designs and owns the outcome of the whole system, agents turn into costs; with an owner, they turn into an operating model.

• • Focus: AI as competitive advantage, not a cost center; managed growth without proportional overhead.
• • Value: in 90 days you have a map — where AI works, where it doesn't, and who owns what.
• • Fear removed: investing in an agent stack and a year later finding parallel pilots with no production outcome and no one accountable at the board level.

CTO / VP Engineering

When you need one person you can trust with people, AI architecture, and delivery — not two separate roles — I cover all three levels with unified accountability for production outcomes. The more mature your agent stack, the more valuable the meta-role: who decides which agents run which tasks, where to stop them, and who owns the outcome of the whole system operating as one model.

• • Focus: delivery predictability + AI embedded in SDLC + architectural trade-offs without accumulating technical debt.
• • Value: C-level peace of mind, less management overhead, AI in the pipeline as team practice — not one engineer's personal habit.
• • Extra layer: owner of the agent system that performs both levels (VP Engineering + Head of AI), not just a person duplicating two roles.

Head of AI / VP AI

When the AI roadmap exists but its translation into engineering practice stalls, I bridge the gap between architectural decisions and how teams actually work every day.

• • Focus: production readiness of AI systems, team enablement, governance, and observability of the AI layer.
• • Value: AI initiatives reach production instead of dying in pilots.

Chief Digital Officer

When the digitalization roadmap needs to land as outcomes, I align business goals with engineering execution without losing control.

• • Focus: AI initiative portfolio execution, transformation risk management, business–technology alignment.
• • Value: digital projects reach production predictably.

HR / Head of Recruitment

A rare combination: engineering leadership + AI systems expertise in one profile — fills roles that would otherwise require two separate people with coordination overhead between them.

• • Focus: lower hiring risk for a complex combined role; explicit JD fit on scale, stack, and scope.
• • Value: a candidate who is easy to defend to CTO and CEO simultaneously.

Chief Architect

When architectural integrity must survive AI transformation, I embed new practices without breaking the foundation or creating unmanageable AI technical debt.

• • Focus: managed architectural debt, AI solution compatibility with the existing landscape, reliable change.
• • Value: platform growth without increasing fragility.

What you get

• • AI as the operating model, not a feature — I diagnose where AI creates real leverage for your engineering organization right now, then embed it across three layers: how teams work, how the product operates, how operations use data.
• • Predictable delivery as a baseline — I start with what breaks delivery now: SDLC bottlenecks, architecture debt, organizational friction. Timelines and quality without black swans.
• • Leadership at scale — multiple teams and leads, clear ownership boundaries, growing people without diluting execution standards.
• • One person instead of two — where VP Engineering and Head of AI don't speak the same language, I cover both levels with unified accountability for business outcomes.
• • Owner of the agent system — I design and own the outcome of the company's whole agent system: where agents give leverage, where they add complexity, where to stop them, who answers to the board. The cheaper agent labor becomes, the more valuable this meta-role.

AI in SDLC

• • AI-assisted coding as team practice — not one engineer's personal habit, but a repeatable workflow with explicit standards and patterns handed to the whole team.
• • AI in testing and review — test case generation, defect analysis, AI in code review: faster cycle without lowering the quality bar.
• • AI-output eval as a release criterion — structured eval before every release that changes model behavior: regression set from production incidents, distribution check on real inputs, one named owner who gives the final go. AI-output quality is verified before release — not accidentally discovered in production.
• • Observability of AI in the production pipeline — model decision logging, human-in-the-loop checkpoints, audit trails; AI in production is visible and managed, not a black box.

AI in Product and Operations

• • Production-ready AI architecture — RAG with proper knowledge structure (not "dump a PDF into a vector DB"), agentic pipelines, recommendation systems; designed for real enterprise constraints: security, legacy, governance.
• • Event-driven AI — LLM as a consumer of event streams (Kafka, webhooks, monitoring alerts, CI events): intelligent event processing, not a chatbot.
• • AI in marketing and business processes — repeatable systems with measurable output, not "let's ask ChatGPT."

Stack and domains

• • AI layer: Anthropic Claude (API, MCP, tool use), OpenAI-compatible interfaces, Gemini; RAG/agentic patterns in enterprise contexts, n8n orchestration, Python + APIs.
• • AI tooling and protocols: Cursor IDE, Claude Code, MCP integrations, and tool routing for engineering and research scenarios.
• • Cloud and platform: AWS, Ansible (IaC), Docker Hub; hybrid / multi-region; virtualization and the VM layer.
• • Integration and events: Kafka, RabbitMQ, APIs, event-driven architecture.
• • Data and analytics: SQL, Elasticsearch, ELK, ClickHouse; vector stores.
• • Quality and delivery: phpunit, Cypress, CI/CD, automated testing pipelines.
• • Enterprise integrations: 1C, Bitrix, ERP / WMS / BI, SAP.

Methods

• • AI Engineering governance: observability, prompt injection defense, data isolation, human-in-the-loop.
• • Engineering governance and operational delivery discipline.
• • Agile planning and execution practices (planning / review / refinement).
• • CI/CD, test automation, quality gates, release reliability.
• • DevOps, IaC, SRE thinking for sustainable operations.
• • Legacy modernization with controlled architectural debt.

Why this matches market expectations

• • I lead multiple teams and managers and build a repeatable delivery model — not heroics from individuals.
• • AI in SDLC is not one-off prompts but a managed operating layer (governance, observability, clear ownership).
• • Positioning is grounded in real production cases: Askona — multi-party integration layer, RAG and event-driven AI for a $680M-revenue retailer; PersonaClick — ML on 199M+ profiles; nextmoveengine.com — my own AI system, running the same patterns I ship for others.
• • In high-pressure environments I keep predictability without artificially slowing growth — through prioritization and transparent trade-offs.
• • The more mature the agent stack, the higher the value of the role shifts: not "doing things agents can't yet do," but designing the agent system and owning its outcome. This meta-role cannot be bought as a SaaS subscription with an SLA — it takes real organizational authority and someone willing to put their name on the result.