Discover
Observe users, workflows, constraints, dependencies, and failure conditions before selecting technology.
Structured public candidate profile
Shayne Winter: systems integration, AI solutions, and technical operations.
A concise view of the cross-disciplinary work Shayne can own, the technical judgment behind it, and the project evidence available for deeper review.
OpportunityEmployment and selected project workExperience20+ years across hardware, software, infrastructure, and operations
Professional summary
Turn ambiguous operating problems into systems people can use, support, and trust.
Shayne works at the boundary between people, process, hardware, software, infrastructure, and AI. He discovers how the work actually happens, defines requirements and authority, directs implementation, and accepts results through evidence in the environment where the system must perform.
Translate
Convert frontline observations into explicit requirements, system boundaries, priorities, and acceptance criteria.
Direct
Set clear technical boundaries, coordinate contributors, review implementation, and retain responsibility for architecture, tradeoffs, correction, and privacy.
Verify
Distinguish a plausible answer from a working result through tests, real executables, hardware, installation, network behavior, and operator acceptance.
Technical judgment
Programming and systems foundations behind the AI-assisted work.
Shayne brings hands-on programming and systems experience to AI-assisted engineering, reviewing code, state persistence, resource behavior, and network interactions and directing corrections toward maintainable solutions.
Code and structure
Read and trace application code, evaluate architecture and implementation choices, and identify approaches that conflict with the system’s requirements.
State and resources
Reason about persistent state, lifecycle behavior, memory, storage, and resource use across applications and services.
Network behavior
Use practical TCP and networking foundations to understand communication paths, load, failure, recovery, and integration behavior.
Current study
Continue deepening knowledge of AI construction and model-context behavior while applying established software and infrastructure judgment.
Capability alignment
Cross-disciplinary work that benefits from technical judgment.
Shayne’s primary identity is Systems Integration & Technical Operations Specialist. His work also aligns with responsibilities found in forward-deployed AI, solutions engineering, AI platforms, and technical product direction without narrowing him to one title.
Forward-deployed AI responsibilities
Use-case discovery, requirements translation, local and connected AI systems, agent workflows, evaluation, user acceptance, and responsible automation.
Systems Integration / Solutions Engineering
Hardware, software, data, networking, operator workflow, deployment, recovery, and supportability across one complete system.
AI Platform / Technical Product Direction
Model and agent lifecycle, durable work, tool authority, privacy boundaries, modular capability growth, and evidence-led release decisions.
Test Engineering / Technical Operations
Programmable instrumentation, Python data analysis, reference-based result review, equipment troubleshooting, fault isolation, manufacturing support, documentation, validation, and reliable handoff.
Selected evidence
Projects that demonstrate the working method.
Each item is linked to a sanitized case study that states current maturity, Shayne’s ownership, evidence level, and the next validation stage.
Working research prototype
Embedded + Windows research system
Pegasus Sense
Five-device wireless-sensing workbench with an operator-facing Windows interface, experiment recording, replay, hardware-in-the-loop exercise, and evidence-led interpretation.
Evidence: software, simulation, physical devices, and observed response across closer and farther conditions.Installed + exercised
Local AI work framework
Pegasus Cerebral
Coordinates local models, specialized semi-agent roles, durable work, tools, recovery, operator decisions, and expandable domain modules.
Evidence: installed Windows runtime, local inference, structured tool use, continued work, and a bounded Books module.Installed + integrated
Continuity + governed operations
Pegasus Cortex
Provides bounded project continuity, durable tasks and knowledge, precise retrieval, recovery, and explicit typed local operations for Cerebral and Codex workflows.
Evidence: installed integration, continuity reads, durable task state, data preservation, and exercised typed operations.Playable prototype
AI-directed product development
Neural Commander
Hands-on playtesting translated into deterministic behavior, versioned content, complex-state validation, native visual evidence, and tracked validation targets.
Evidence: real Windows runs plus broad deterministic test coverage.Controlled local research
Geometric / latent reasoning
Local model-efficiency research
Paper-derived ideas converted into bounded training and evaluation experiments on an NVIDIA DGX Spark using frozen baselines, matched comparisons, quality review, and promotion gates.
Evidence: controlled runs rejected attractive efficiency signals when answer quality did not hold.Field deployed
Connected product
Cellular virtual-tour kiosks
Three repeatable touch, video, cellular, and battery-backed units specified, assembled, programmed, deployed, and supported in field service.
Evidence: completed installations, sanitized field photograph, at least one year of service.Multi-site deployment
Distributed business software
Offline-capable retail platform
Visual Basic transaction and site-integration platform that kept several stores working during central-connectivity interruptions and synchronized results later.
Evidence: real office, call-center, store, register, peripheral, network, and support operation.Skills and technology
Current engineering capability in context.
C#, .NET, Python, PowerShell, JavaScript, TypeScript, SQL, and the supporting platforms below appear across current and prior project work, automation, diagnostics, integrations, test systems, and operating tools.
AI systems
- Forward-Deployed AI Solutions
- AI-directed engineering
- Local AI infrastructure
- AI agent systems
- Human-in-the-loop AI
- Model evaluation
- Controlled local training research
- Context and knowledge systems
- Tool and authority boundaries
- Privacy-aware automation
Systems and product
- Systems integration
- Requirements engineering
- Frontline discovery
- Workflow mapping
- Technical product direction
- Root-cause analysis
- Stakeholder translation
- Acceptance criteria
- Failure and recovery design
- Supportable handoff
Software
- C#
- .NET
- Python
- PowerShell
- Java
- JavaScript
- TypeScript
- Visual Basic
- VBA
- SQL and SQLite
- WinForms and WPF
- React / Next.js
- Node.js
- Godot
Infrastructure and field systems
- Windows infrastructure
- Hyper-V
- Networking and VPN
- Public HTTPS operations
- Embedded systems
- ESP32-S3
- Wireless sensing
- e-ink displays
- Cellular-connected products
- Data acquisition
- Hardware-in-the-loop evaluation
- Deployment and recovery
Test engineering and instrumentation
- Environmental-test and thermal-chamber troubleshooting support
- Test-result review using supplied procedures and limits
- Test-setup repair assistance
- Programmable data acquisition
- Instrument control and data collection
- Materials, shear-force, and stress-test equipment
- Python test-data analysis
- Repeated-run comparison
- Sensor and instrumentation fault isolation
Verification
- Unit and integration testing
- Executable acceptance
- Native Windows runtime review
- Hardware exercise
- Installation and repair
- Network validation
- Diagnostics and observability
- Regression testing
- Evidence-layer discipline
- Known-failure tracking
Professional ownership
Clear accountability. Evidence that stands up in an interview.
Shayne owns the decisions that turn an unclear need into an accepted result: discovery, requirements, system boundaries, architecture tradeoffs, specialist direction, correction, and final validation. AI expands implementation capacity while human judgment remains accountable for what is built and whether it works.
Professional identity
Systems Integration & Technical Operations Specialist whose demonstrated work spans AI solutions, platform direction, connected products, software, infrastructure, and technical operations.
Evidence model
Case studies identify the actual maturity and validated scope of each system—research, prototype, tested, installed, or deployed—so technical depth remains credible and easy to discuss.
Professional context
Sanitized employer and client descriptions preserve the system challenge, ownership, transferable capability, and operating result.
Start a conversation
Connect on LinkedIn to discuss employment, AI solutions, systems integration, technical operations, technical product work, or selected contract work.