Grigoriy Dobryakov

AI-Committed Organizations

Companies that have moved past the "exploring AI" stage and made a board-level decision: AI becomes the operating model, not a single-department initiative. They need a permanent owner — someone who is simultaneously accountable for people, delivery, and AI being embedded at every layer.

• • Key benefit: AI embedded in the organization, not bolted on top.
• • Expert role: permanent function owner, not a consultant with a roadmap.
• • Message: where companies typically hire two people who don't speak the same language — one person with unified accountability.
• • Engagement shape: priority is fulltime; entry via 30-day AI Engineering Diagnostic with a concrete deliverable.
• • Typical profile: 150–1500 employees, PE-backed or post Series B with a board-level AI mandate.

Enterprise in AI Transformation

Large corporations with legacy landscapes where the cost of failure is high and business processes must not break during AI adoption. Focus: safe AI integration into running systems, controlled pace of change, and predictable timelines under governance constraints.

• • Key benefit: AI in production without losing business continuity.
• • Expert role: architectural and managerial guarantor of stability during transformation.
• • Message: AI is embedded into the existing landscape with explicit governance rules, not a "big bang" replacement.
• • Engagement shape: fulltime as HoE or Director of Engineering; with headcount limits — fractional with clear scope and practices handover.

AI Scale-up (Series B/C)

Fast-growing companies where the product is already flying, but AI practices are ad hoc: parallel initiatives, none in production, no clear owner. Focus: building an AI-driven engineering culture without losing development pace.

• • Key benefit: AI in SDLC, product, and operations as a repeatable system — not one engineer's magic.
• • Expert role: the adult in the room for founder-led companies, turning AI experiments into production practice.
• • Message: mature AI engineering as an accelerator, not a constraint — speed with control.
• • Engagement shape: mostly fulltime for scaling; fractional as a deliberate 3–6 month bridge with explicit responsibility boundaries.

Traditional Business (banks, retail, industry)

Companies that want measurable results from AI with a pragmatic approach and clear economics of change. Focus: cost reduction, profitability growth, and reliable transformation without chasing hype.

• • Key benefit: AI as profit, not hype.
• • Expert role: a bridge between proven practices and the new technology wave.
• • Message: predictable outcomes in business language — unit economics, throughput, and cost-to-serve.
• • Additional focus: when operational scale starts pressuring margins.

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.

• • 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.

CTO / VP Engineering

When you need one person you can trust with people, AI architecture, and delivery — not two separate hires — I cover all three levels with unified accountability for production outcomes.

• • Focus: delivery predictability + AI embedded in SDLC + architectural trade-offs without accumulating technical debt.
• • Value: C-level peace of mind, less management overload, AI in the pipeline as team practice — not one engineer's personal habit.

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 manageability.

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

HR / Head of Recruitment

A rare combination: engineering leadership + AI systems expertise in one profile — fills roles that would otherwise require two hires 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.

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.
• • 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 — RAG and event-driven AI at $680M+ scale; PersonaClick — ML on 199M+ profiles; nextmoveengine.com — own AI system as "eat what I cook."
• • In high-pressure environments I keep predictability without artificially slowing growth — through prioritization and transparent trade-offs.