Yuriy Shuldeshov
Principal Engineer
Principal-level engineer operating at organization scale
I define long-term architectural direction, align engineering standards across teams, and design resilient distributed and AI-native systems that scale technically and organizationally.
Specialized in platform strategy, system decomposition, multi-tenant architectures, data platform design (OLTP/OLAP), and production-grade RAG systems with reliability and cost governance.
Key Achievements
Organization-scale impact across platform architecture, distributed systems, and engineering standards
Platform Standardization
Unified CI/CD and delivery practices across engineering organization, reducing deployment time from 4h to 15m and eliminating cross-environment drift
Reliability Transformation
Established observability framework and incident governance model, increasing uptime from 94% to 99.8–99.95% across fintech platform handling 100K+ daily transactions
System Decomposition
Architected monolith → microservices transition with multi-tenant design, API governance, and versioning strategy adopted across 3 product teams
High-Load Systems
Scaled distributed systems from 1K to 10K+ RPS via architectural redesign, caching strategy, and traffic management for e-commerce platform (1M+ users)
Cloud & Cost Governance
Defined cloud resource strategy and autoscaling models, reducing infrastructure spend by 40–60% while improving scalability across AWS/GCP environments
AI Platform Foundations
Designed production-grade RAG/LLM architecture with auditability, retrieval traceability, and cost control for AI-first product initiatives
Organization-Level Impact
Cross-team architecture governance and engineering maturity
- Defined cross-team architecture governance model establishing decision-making framework and alignment process
- Established API versioning and compatibility standards adopted across product engineering
- Introduced ADR process for long-term technical consistency and knowledge retention
- Designed platform evolution roadmap (3-year horizon) balancing innovation and stability
- Reduced architectural entropy across distributed teams through standardization and documentation
Core Expertise
Strategic capabilities and technical foundation
Strategic Architecture
Cloud & Infrastructure
Data Platform Strategy
AI Systems Architecture
Observability & Reliability Engineering
Engineering Standards
Work Experience
15+ years of progressive leadership in technology and engineering
Principal / CTO Advisory
Multiple Engagements
Platform architecture strategy across 15+ startups from MVP to Series A
- Defined long-term technical direction and cloud-native infrastructure patterns adopted across teams
- Architected cross-team platform standardization and API governance frameworks
- Designed multi-tenant systems with scalability strategy for SaaS platforms
- Established data platform architecture (OLTP/OLAP integration) for analytics-driven products
- Led AI/ML systems design: production RAG pipelines with cost control and traceability
- Unified engineering standards and documentation practices across distributed teams
VP Engineering / CTO — Fintech
Confidential NDA
Platform evolution from monolith to distributed architecture
- Drove reliability transformation: 94% → 99.8–99.95% uptime across payment platform (100K+ daily transactions)
- Architected system decomposition strategy and API governance for monolith → microservices transition
- Unified engineering processes and introduced CI/CD standards reducing deployment time 4h → 15m
- Established observability framework and incident response model across organization
- Scaled team 8 → 25 engineers with defined career paths and engineering standards
- Aligned compliance and security (PCI DSS Level 1) with development velocity
Engineering Leadership — Enterprise & E-commerce
Multiple Organizations NDA
Scalable infrastructure for high-load systems (1M+ users)
- Designed high-load architecture scaling from 1K → 10K+ RPS via caching strategy and traffic management
- Implemented cloud cost optimization: 40–60% infrastructure savings while improving scalability
- Built GitOps and infrastructure-as-code practices adopted across engineering teams
- Established Kubernetes platform strategy and adoption roadmap for cloud-native migration
- Achieved 99.9% uptime supporting peak traffic events (10x normal load)
Featured Projects
Selected case studies with measurable business impact
System Decomposition Strategy
Fintech | Organization-wide initiative
- Strategic Context: Defined system decomposition strategy and organizational migration roadmap adopted across engineering teams
- Architectural Impact: Multi-tenant design, API governance, versioning standards enabling autonomous team deployments
- Organizational Outcome: Platform coherence -60% coupling, team velocity +40%
Distributed Systems Scaling
E-commerce | Platform evolution
- Strategic Context: Designed architectural approach for high-load growth supporting 10x traffic increase
- Architectural Impact: Caching strategy, traffic management patterns, reliability framework adopted by platform teams
- Business Outcome: Peak capacity 10K+ RPS, zero-downtime releases, revenue-critical events supported
Platform Standardization
SaaS | Cross-team initiative
- Strategic Context: Unified deployment practices and infrastructure patterns across engineering organization
- Organizational Impact: Eliminated configuration drift, established recovery playbooks, standardized delivery pipeline
- Efficiency Gain: Deployment frequency 20x increase, operational overhead -75%
Reliability Engineering Framework
Fintech | Observability transformation
- Strategic Context: Established observability framework and incident governance model across platform
- Architectural Impact: SLO-driven design principles, distributed tracing strategy, MTTR optimization patterns
- Business Outcome: Uptime 94% → 99.8%, incident resolution -70%, compliance readiness achieved
AI Platform Architecture
AI/ML Product | Production infrastructure
- Strategic Context: Designed production-grade RAG/LLM architecture with retrieval evaluation and cost governance
- Architectural Impact: Confidence scoring models, human-in-the-loop orchestration, auditability frameworks
- Technical Outcome: Inference latency p99 < 500ms, cost predictability, compliance-ready traceability
Cloud Cost Governance
SaaS Scale-up | Resource optimization
- Strategic Context: Defined cloud resource strategy and autoscaling models for organization-wide adoption
- Architectural Impact: Capacity planning frameworks, cost allocation models, rightsizing automation adopted by teams
- Financial Impact: Infrastructure spend -40–60%, predictable scaling, runway extension enabled funding
Engineering Philosophy
- Architecture must scale organizationally, not only technically.
- Platform coherence reduces long-term complexity.
- Observability is a design property, not an operational afterthought.
- AI systems in production must be deterministic, auditable, and cost-aware.
Architectural Scope
I operate across:
Multiple Product Teams
Cross-team architecture alignment and dependency management
Cross-Functional Alignment
Engineering / Product / Data collaboration frameworks
Platform Abstraction Layers
Coherent interfaces reducing organizational coupling
Long-Term Technical Risk Mitigation
Proactive entropy reduction and tech debt governance
Engineering Maturity Evolution
Capability development across reliability, observability, and delivery practices
Get In Touch
Open to Principal / Staff+ roles focused on platform architecture and AI systems design in high-growth engineering organizations
Contact Information
Telegram
GitHub
Location
Remote (UTC+3) | Open to relocation for the right opportunity
Currently Available
Open to full-time CTO roles, part-time advisory, and consulting projects. Prefer remote-first companies with strong engineering culture.