I turn AI ideas into production-ready software. I build scalable SaaS products, AI agents, RAG workflows, backend services, data pipelines, and cloud infrastructure—always with a focus on measurable outcomes and systems teams can maintain long-term.
My approach combines product-minded engineering, end-to-end ownership, and clear communication. I work across the full software development lifecycle: understanding the business goal, shipping the smallest useful version, and hardening it for reliability and scale.
- 🚀 Building full-stack SaaS and AI systems from idea to production
- 🤖 Designing AI agents, RAG applications, document AI, and practical workflow automation
- ⚡ Modernizing APIs, data flows, and event-driven backend systems for performance and scale
- 🧩 Creating reusable frontends, design systems, and micro frontend architectures
- ☁️ Owning cloud infrastructure, CI/CD, testing, observability, and production delivery
- 🧑🏫 Leading engineering teams, mentoring developers, and teaching complex technical concepts clearly
- Increased revenue by up to 40% with predictive budget-spend systems
- Created more than $500K in annual efficiency gains through AI workflow automation
- Saved approximately 5,000 hours per year with real-time validation and access-control automation
- Improved Node.js and GraphQL API performance by up to 90%
- Reduced test-build-deploy cycles by 40% through CI/CD and infrastructure improvements
AI and automation: OpenAI, Claude, Gemini, AI agents, LLM applications, RAG, vector search, OCR, n8n, human-in-the-loop workflows
Frontend: React, Next.js, TypeScript, micro frontends, design systems, backend-for-frontend architecture
Backend and data: Node.js, Python, GraphQL, REST APIs, PostgreSQL, MySQL, MongoDB, Prisma, event-driven systems
Cloud and delivery: AWS, GCP, Docker, Kubernetes, Terraform, CI/CD, testing, observability
Integrations: CRM, Slack, email, calendar, webhooks, and third-party APIs
- Product-minded engineering: solving real workflow and business problems—not building demos without a path to value
- End-to-end ownership: carrying architecture, implementation, deployment, and handoff through to a production outcome
- Pragmatic AI: combining deterministic software with AI where it adds leverage, keeping systems inspectable and people involved where judgment matters
- Maintainable delivery: building reliable, tested systems that teams can own, operate, and extend