Grigoriy Dobryakov

Enterprise in AI Transformation

Large corporations with legacy landscapes where the cost of failure is high and business processes must not break during adoption of new technologies. Focus: safe AI integration, controlled pace of change, reducing systemic risk, and predictable timelines under governance constraints and executive alignment.

• • Key benefit: fewer delivery black swans.
• • Expert role: risk insurance for CTO/VP Engineering.
• • Message: stability without losing momentum.
• • Engagement shape: typically full ownership in-role; with headcount limits, fractional with clear scope, timeline, and handover to an internal owner after stabilization.

AI Scale-up (Series B/C)

Fast-growing companies where the product is already flying, but processes and engineering operations cannot keep up with scale. Focus: turning chaotic development into a mature value-delivery machine without bureaucracy.

• • Key benefit: a growth foundation without slowing teams down.
• • Expert role: the adult in the room for founder-led companies.
• • Message: speed with control - mature SDLC as an accelerator, not a constraint.
• • Engagement shape: mostly full-time for scaling; fractional as a deliberate 3–6 month bridge to hiring a permanent EM, with explicit responsibility boundaries and availability.

Traditional Business (banks, retail, industry)

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

• • Key benefit: AI as profit, not hype.
• • Expert role: a bridge between proven practices and the new tech wave.
• • Message: predictable results in business language: unit economics, throughput, and cost-to-serve.
• • Engagement shape: project-based delivery with measurable milestones; when needed, diagnostics plus a time-boxed pilot with clear economics for the business sponsor.

CEO / Founder

When you need a managed engineering system that supports business growth, I stabilize delivery and free your time for strategy.

• • Focus: predictable delivery, economic impact, delegating operational chaos.
• • Value: measurable cost reduction and profitability growth—or product scaling without manual micromanagement.

Chief Digital Officer (CDO)

When you need the digitalization roadmap to land as outcomes, I align business goals with engineering execution without losing manageability.

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

Professional Buyer

When timelines, budget, and execution quality must be locked in, I provide verifiable delivery discipline and lower procurement risk.

• • Focus: transparent agreements on timelines, quality, and outcomes.
• • Value: a dependable choice for management services in high-cost-of-failure contexts.

CTO / VP Engineering

When you need faster delivery without losing control, I take ownership of reducing technical and organizational risk.

• • Focus: release predictability, speed/quality balance, resilient delivery.
• • Value: C-level peace of mind and less management overload.
• • Outcomes contract: what changes within 30–90 days and which metrics (timelines, stability, team load) define success.

Chief Architect

When architectural integrity must survive transformation, I embed new practices without breaking the foundation.

• • Focus: managed architectural debt, solution compatibility, reliable change.
• • Value: platform growth without increasing fragility.

HR / Head of Recruitment

When you need a strong engineering leader who clears both formal requirements and culture fit, I provide a verifiable profile and a clear value narrative.

• • Focus: lower hiring risk, mature leadership, team stability.
• • Value: a candidate who is easy to defend to both business and engineering stakeholders.
• • First screen: explicit JD fit (stack, scale, scope) plus a clear format—full-time or fractional with boundaries and horizon.

What you get

• • A calm delivery operating rhythm — timelines and quality without surprises: visible risks, disciplined releases, controlled incidents and SLAs.
• • Leadership at scale — multiple teams and leads, clear ownership boundaries, growing people without diluting execution standards.
• • Tech, product, and economics connected — architectural trade-offs are explicit for time-to-market, cost-to-serve, and sustained quality.
• • AI as an accelerator under governance — not one-off prompts, but repeatable workflows that reduce shadow-AI risk and speed up organizational learning.

How I deliver the outcomes

• • Teams and people maturity — hiring and growing leads and engineers, goals and feedback, less churn and firefighting through clear rituals.
• • End-to-end delivery — SDLC from backlog to production: release predictability, incidents, postmortems, continuous process improvement.
• • Platform and architecture — balancing speed, reliability, and total cost of ownership; high availability and integrations without growing fragility.
• • Stakeholder alignment — one outcomes language for CTO, C-level, product, HR, and engineering around measurable results, not vanity roadmaps.

AI and automation

• • Systematic LLM workflows and agents — tasks, search, and data in one operating loop: repeatability, observability, clear accountability—patterns the whole team can adopt.
• • AI-assisted SDLC in production — design, code, tests, log and incident analysis with LLM support plus practices handed to the team.
• • Data and intelligence — parsing and transforming data, fact-backed decisions, deep research via LLM + search for prioritization and business communication.
• • Hypothesis and communication checks — role-based and ATS-like simulations before expensive steps (releases, hiring, external commitments).
• • Fast prototyping loops — event-driven chains and a shorter idea → working artifact cycle where it speeds validation without bypassing engineering discipline.

Stack and domains

• • AI layer: ChatGPT, Gemini; AI coding workflows; meeting intelligence; n8n; Python and APIs; research/search agents; NotebookLM for media and learning formats.
• • Cloud and platform: AWS, AWS CDK, Ansible, Docker Hub, hybrid / multi-region.
• • Integration and events: Kafka, RabbitMQ, APIs, event-driven architecture.
• • Data and analytics: SQL, JSON, ElasticSearch, ELK, ClickHouse.
• • Quality and enterprise: phpunit, cypress, automated pipelines; 1C, Bitrix, ERP / WMS / BI, SAP integrations.

Methods

• • Engineering governance and operational delivery discipline.
• • Agile planning and execution practices (planning / review / refinement).
• • CI/CD, test automation, quality gates, release reliability.
• • DevOps, IaC, observability, and SRE thinking for sustainable operations.
• • Legacy modernization with controlled architectural debt.
• • Workflow automation and AI orchestration: pipeline design, LLM integration with APIs and external sources, research → synthesis → action loops.

Why this matches market expectations

• • I lead multiple teams and managers and build a repeatable delivery model—not heroics from individuals.
• • I strengthen platform resilience: SRE/DevOps, security, reliability, recoverability—in the language of risks and metrics sponsors understand.
• • In high-pressure environments I keep predictability and quality without artificially slowing growth—through prioritization and transparent trade-offs.
• • Positioning is grounded in real cases and public materials: measurable delivery outcomes, not stack bragging.
• • I embed AI as a managed operating layer (governance, observability, clear ownership)—not as a substitute for engineering discipline.