Fractional CTO · Engineering audit · Practical AI
Engineering & AI Transformation Consultant
I help companies improve engineering delivery, service quality, process transparency, and pragmatic AI adoption — from diagnosis to hands-on implementation.
13+ years in engineering leadership as CTO, Unit Tech Lead, and Head of Development. I have scaled teams from 3 to 30 people, built internal tooling for operationally critical systems, introduced delivery and onboarding processes, and implemented AI-assisted workflows that help small teams operate above their headcount.
How I can help
I work with founders, CTOs, product and operations leaders who need an experienced technical partner to diagnose messy systems, improve engineering execution, and turn AI from a vague initiative into practical workflow improvements.
Engineering & Delivery Audit
I review how work moves through the organization: planning, ownership, delivery, incidents, technical decision-making, documentation, and feedback loops. The output is a clear map of bottlenecks, risks, quick wins, and structural improvements.
Service Quality & Process Transparency
I help teams make operational quality visible: responsibilities, escalation paths, metrics, recurring issues, internal SLAs, knowledge base hygiene, and communication between engineering, product, operations, and support.
Pragmatic AI Adoption
I identify where AI can reduce manual work, improve analysis quality, support decision-making, or speed up internal workflows. The focus is not “adding a chatbot”, but finding real process leverage.
Internal Tooling & Automation
I help design and launch internal tools, scripts, dashboards, knowledge bases, and AI-assisted workflows that remove repetitive work and make operations more reliable.
Fractional CTO / Technical Advisor
I support founders and leadership teams with architecture, engineering strategy, hiring, team structure, technical risk, vendor decisions, and difficult trade-offs.
Engineering Leadership System
I help set up hiring, onboarding, 1:1s, performance cycles, team lead growth, technical committees, decision-making rituals, and healthy ownership models.
Typical requests
“Our team is growing, but transparency is getting worse.”
“Everyone is busy, but it is hard to understand what actually moves.”
“We want to adopt AI, but do not want a useless corporate chatbot.”
“Support, operations, or analysts still do too much manual work.”
“Engineering is constantly firefighting.”
“We need a senior technical perspective before scaling the team.”
“We have a technical team, but need a CTO-level sparring partner.”
“We need to understand where automation will have the fastest effect.”
Engagement formats
Fast Diagnostic
Interviews, process review, artifact review, and a focused analysis of current bottlenecks. You get a clear problem map, quick wins, risks, and a recommended improvement plan.
AI / Automation Discovery
A practical search for workflows where AI or automation can create measurable leverage. You get prioritized use cases, MVP proposals, implementation risks, and a realistic next-step roadmap.
Implementation Support
Hands-on support with internal tooling, AI-assisted workflows, engineering process changes, delivery practices, knowledge base improvements, or team rituals.
Fractional CTO / Advisor
Regular work with founders, CTOs, product leaders, operations leaders, or team leads on strategy, architecture, hiring, process design, technical risks, and execution.
Selected outcomes
A few concrete examples of the operating systems, teams, and products I have built or improved across fintech, B2B SaaS, AI products, and engineering organizations.
- Scaled a cross-functional unit from 3 to 30 people at Uzum.
- Grew two team leads who now run their own teams independently.
- Built internal tooling for bank operations and card issuance under tight reliability requirements, enabling 40,000+ cards issued.
- Established hiring, delivery, onboarding, 1:1, 360 review, and performance processes.
- Built an internal knowledge base that evolved into an AI-assisted tool for customer care and engineering teams.
- Introduced AI-assisted tooling to automate repetitive workflows and improve delivery speed.
- Built and grew an engineering team from scratch to 11–20 people at Worki / VK Rabota.
- Led development of a B2B SaaS product for feedback analysis and review processing, from product ideas to customer-data demo stands.
- Turned messy customer feedback and review data into structured analysis flows that could be shown to real client teams.
- Built AI-native development practices around prompt regression, output benchmarking, automated review cycles, and faster product iteration.
- Designed client-facing conversational data experiences where users could explore analysis results and “talk to data” instead of reading static reports.
- Connected real customer datasets into demo environments so product value could be evaluated on actual business context, not abstract examples.
- Improved delivery transparency by making ownership, priorities, technical decisions, and feedback loops explicit across product and engineering.
- Created operational workflows where support, customer care, and engineering could share context through cleaner knowledge and tooling.
- Owned architecture and delivery trade-offs for products where reliability, iteration speed, and operational clarity mattered at the same time.
- Built monetization systems and B2B integrations at Worki / VK Rabota while also growing the engineering team and operating model.
- Owned infrastructure scaling and reliability work for a growing product organization.
- Helped small teams use automation and AI-assisted workflows to operate with more leverage than their headcount normally allows.
AI without theatre
I do not start with “let’s add AI”. I start with the process: where people lose time, where quality drops, where knowledge is scattered, where decisions are delayed, and where existing data does not turn into action.
AI is useful when it reduces repetitive work, improves analysis quality, speeds up research, keeps knowledge bases alive, supports better decisions, or helps a small team operate with more leverage.
The goal is not to replace judgment. The goal is to make good people faster, better informed, and less overloaded.
Who I help
Experience snapshot
Led development of a B2B SaaS product for analyzing and processing customer feedback and reviews. Owned the full cycle from product ideas and AI-native delivery practices to demo stands with connected client data, analysis results, and a client-facing TUI for talking to data.
Scaled a cross-functional fintech unit from 3 to 30 people, built internal tooling for bank operations and card issuance, established delivery and onboarding processes, grew team leads, and introduced AI-assisted knowledge workflows.
Built the engineering team from scratch to 11–20 people, introduced hiring and performance processes, built monetization systems and B2B integrations, and owned infrastructure scaling and reliability.
Detailed background
Responsibilities
- Led development of a B2B SaaS product for feedback analysis and review processing, offered to companies on the market
- Owned the full cycle from product ideas and technical discovery to working demo stands with connected client data
- Built workflows that transformed customer feedback data into structured analysis, visible results, and actionable product output
- Introduced AI-native product and engineering practices across discovery, development, testing, review, and delivery
- Used AI both inside the development process and in the end-user product experience
- Developed a client-facing TUI experience that allowed customers to talk to their data and explore analysis results conversationally
- Designed development and testing practices built around AI assistance, including prompt regression, output benchmarking, and automated review cycles
- Owned engineering decisions across architecture, delivery, iteration speed, demos, and client-data integration
Responsibilities
- Scaled a cross-functional unit from 3 to 30 people (engineers, analysts, ops); grew two team leads who now run their own teams independently
- Built and shipped internal tooling for bank operations and card issuance under tight reliability requirements, enabling 40,000+ cards issued
- Designed backend services and architecture to handle operational load in a fintech environment where failures are not an option
- Introduced an architectural committee to distribute technical decision-making across the unit
- Established hiring, delivery, and onboarding processes across the unit
- Built an internal knowledge base that evolved from static docs into an AI-assisted tool for customer care and engineering teams
- Introduced AI-assisted tooling into team workflows to automate repetitive processes and improve delivery speed
- Worked closely with InfoSec on compliance and security practices; ran internal technical trainings
Responsibilities
- Progressed from Back-end Engineer to Head of Development within one year
- Built and grew the engineering team from scratch to 11–20 people; established hiring processes, 1-to-1s, 360 reviews, and performance cycles
- Built a flexible monetization system that enabled new revenue streams and self-serve B2B client integrations across multiple business cases
- Led migration and rewrite of services to Elixir and Ruby; owned infrastructure scaling and site reliability for a platform with hundreds of thousands of users
- Forked and extended prerender.io to support external headless Chrome instances; deep work on NATS and NATS Streaming
- Built custom NATS clients for Ruby and Elixir; developed a lightweight ClickHouse package for internal use
Responsibilities
- Support of merryjane.com, implementation of new features for backend (Rails) and frontend parts (React.js, Next.js). Changed all deployment scheme from Dokku at EC2 to AWS EBS, wrote a small deployment tool for automatic creation of everything using AWS credentials(tool is based on boto3).
- Consulting and leading Python developers at Django project.
- Development of private framework on Rails 5+GraphQL at backend and React.Native+Apollo at frontend.
- Developed an SEO application for estimating costs using different SDKs in Python+Django.
- Led a team of 2 backend and 2 frontend developers; introduced dry-transaction patterns and built a flexible, well-tested system.
- Built a small crypto project for automated trading of cryptocurrencies using signals from different sources.
Responsibilities
- Analysis of applications on Ruby and Python, implementing new features at Ruby and Python flask applications, covering RoR project with tests (Rspec), interaction with UI (React) at https://dailypro.com
- Development of django application and jupyter at prototype of https://auger.ai/ called deephub at http://deephub.com/
Responsibilities
- Creating UI for scientific project with 3D objects
- Simulating atomic and molecular interaction
- Creating back-end services with Python and Ruby on Rails
2011-2016 was a progression from software development into backend work, game development, and product engineering. Key roles included Software Developer at PromService, PHP Developer at Involta, Ruby/ActionScript Developer at Social Quantum, Ruby on Rails Developer at 404 Group, Unity3D Developer at Atomic Works, and Senior Software Developer at Deep Learn Inc.
Skills
Engineering
- Ruby
- Elixir
- Python
- JavaScript
- Rails
- Django
- Flask
- GraphQL
- REST APIs
Data & Infrastructure
- PostgreSQL
- Redis
- ClickHouse
- NATS
- AWS
- Docker
- Git
AI & Tooling
- LLM pipelines
- Agentic workflows
- Analysis pipelines
- Detection systems
- RAG
- Prompt engineering
Education
Formal education in software engineering.
Let’s discuss where better engineering, clearer processes, or practical AI can create leverage.
If your team is growing, overloaded, stuck in delivery fog, or trying to understand where AI can actually help, I can help diagnose the situation and turn it into a practical plan.