I turn ambiguous, high-stakes business and operational problems into production software. As the founder of Forge Work Engineering, I own the complete outcome: requirements discovery, system architecture, implementation, integrations, infrastructure, deployment, observability, troubleshooting, and long-term reliability.
My work spans AI and agentic systems, distributed services, full-stack platforms, automation, quantitative and financial systems, cloud infrastructure, and complex third-party integrations. I work across technical and nontechnical boundaries, translating rough objectives and domain knowledge into systems people can safely operate and depend on.
I use AI-native engineering workflows to increase execution speed while retaining responsibility for architecture, acceptance criteria, validation, security, and production behavior.
- AI and agentic systems coordinating models, agents, tools, context, memory, workflows, and external services
- Full-stack SaaS and platform systems spanning user experience, APIs, data, infrastructure, and operations
- Distributed and event-driven services designed for reliability, performance, failure isolation, and recovery
- Automation that turns fragmented operational processes into governed, observable execution paths
- Cloud infrastructure, deployment pipelines, monitoring, and operational tooling
- Modernization of complex systems into modular, testable, maintainable architectures
Nerva is a proprietary, model- and provider-agnostic command system for governed AI work. It provides a universal core and adaptive Mission Control through which people can turn objectives into coordinated operations across agents, models, tools, context, memory, workflows, and existing systems.
Rather than making chat the product, Nerva centers the operation—the complete path from intent and context through execution, evidence, review, and recovery—so people can delegate meaningful work without surrendering visibility or control.
Building Nerva requires translating a broad, unsolved problem into a coherent product model, system architecture, integration and data boundaries, operator experience, governance model, and reliability requirements.
- Governing a Software-Engineering Objective Across 53+ Hours of Active Execution — An internal-production case study of Build Ops: governed execution across linked runs with durable operating state, explicit authority boundaries, independent validation, interruption, and recovery.
- Designing the Command Layer for Agentic Work — A non-proprietary engineering reasoning brief showing how I translated agentic-system failure modes into operational invariants, tradeoffs, and recovery design for Nerva.
Both work samples intentionally omit proprietary source code, prompts, schemas, infrastructure, security mechanisms, and implementation details.
- Languages: Python, TypeScript/Node.js, Rust, Go, Solidity
- AI systems: Model and provider abstraction, agent orchestration, tool integration, retrieval and grounding, structured outputs, evaluation, fallback, recovery, and observability
- Backend and APIs: REST, GraphQL, WebSockets, event-driven systems, microservices, monorepos, and third-party integrations
- Frontend: React, Next.js, Vue, React Native, and Electron
- Data: PostgreSQL, MySQL, MongoDB, and Redis
- Cloud and infrastructure: AWS, Azure, Google Cloud, Docker, Kubernetes, Terraform, CI/CD, monitoring, and incident readiness
- Specialized systems: Quantitative execution, payment infrastructure, blockchain/DeFi, EVM ecosystems, and compliance-aligned platforms
- Clarify the business objective, operational constraints, and real failure surface.
- Define system boundaries, success criteria, and what should—and should not—be automated.
- Model the complete execution path before committing to implementation.
- Build in production-ready increments with clear interfaces, validation, observability, and recovery.
- Explain technical constraints and tradeoffs plainly so stakeholders can make informed decisions.
- Remain accountable after the demonstration becomes a production system.
Most of my original production work is private because it contains proprietary business logic, unreleased product IP, and sensitive implementation details.
The public forks on this account are working copies of open-source projects retained for evaluation, adaptation, experimentation, or potential integration. They are not presented as my original authorship.
I am interested in senior AI systems, solutions engineering, forward-deployed engineering, platform engineering, and full-stack roles where I can own the path from an unclear problem to a reliable production outcome.