Physical truth first
Start with signals, tolerances, interfaces, and failure modes. Software is strongest when it respects the system beneath it.
- Electronics
- Test engineering
- Root-cause analysis
Living engineering profile · 2026
Yevgeni AminovEngineer of connected systems.
I turn physical constraints, operational needs, and emerging technology into systems that can be tested, understood, and improved.
YA
systems engineer
Hardware
Automation
Edge
AI
- Foundation
- Electronics & validation
- Method
- Systems thinking
- Direction
- Human-centered AI
01 / CORE
Four Tech DNA pillars
The recurring principles behind the work—not a list of tools, but a way of engineering.
Structure the unknown
Translate ambiguous problems into observable states, clear boundaries, and repeatable verification paths.
- System mapping
- ATP design
- Documentation
Automate with intent
Remove repetitive effort while keeping operators informed, in control, and able to diagnose what happens next.
- PowerShell
- Telemetry
- Safe workflows
Make intelligence useful
Apply AI where it sharpens engineering judgment: at the edge, inside workflows, and with explicit fallback paths.
- Local LLMs
- Hybrid cloud
- AI orchestration
02 / METHOD
Engineering thinking workflow
A practical loop that moves from field reality to durable learning.
- 01
Observe
Read the environment, constraints, symptoms, and human context.
- 02
Model
Define interfaces, dependencies, risks, and measurable outcomes.
- 03
Prototype
Build the smallest system that can challenge the assumptions.
- 04
Verify
Exercise normal, boundary, and failure paths with evidence.
- 05
Encode
Turn the lesson into automation, documentation, and the next iteration.
03 / EVIDENCE
Selected engineering systems
ENG-Portal projects are working evidence of an evolving engineering practice.
✓
ATP / AI Simulation
Transforms formal test procedure thinking into an AI-assisted proof of concept—connecting verification discipline with new interfaces.
Inspect system ↗ >_PowerShell Systems Cockpit
A command and telemetry workspace for hardware auditing, endpoint insight, and staged operational workflows.
Inspect system ↗ ◎Architecture Memory
A living engineering knowledge system that preserves architectural context, technical decisions, system relationships, and lessons learned across the evolution of ENG-Portal.
Inspect system ↗ ⌁ESS Lab Simulation
An environmental stress screening interface that makes profiles, stages, and test conditions legible to operators.
Inspect system ↗04 / TRAJECTORY
From Hardware to AI
The layers accumulated. The engineering foundation stayed constant.
- Foundation
Electronics & hardware
Components, interfaces, measurements, and the discipline of physical evidence.
- Systems
Verification & reliability
Repeatable procedures, environmental screening, and failure-oriented thinking.
- Automation
Operations as software
Scripts, telemetry, and operator tools that make complex work repeatable.
- Infrastructure
Edge & hybrid architecture
Proxmox, local GPU inference, secure routing, and resilient cloud fallback.
- Now
Applied engineering intelligence
AI as a collaborator inside governed, observable, human-owned systems.
05 / CURRENT VECTOR
Build intelligence that understands the system.
My current mission is to connect deep engineering context with practical AI: local where it matters, cloud where it helps, and transparent enough to trust.
Learning direction
- 01Agentic engineering workflows
- 02Edge AI orchestration
- 03Reliable human–AI interfaces
- 04Knowledge systems for technical teams
Connection point / available online
Let’s compare notes on engineering systems.
Explore the working prototypes in ENG-Portal or connect with Yevgeni on LinkedIn.