I build production-ready backend and AI systems for SaaS products using Python, Go, PostgreSQL, and AWS.
My focus is the engineering layer between an AI prototype and a dependable production product: secure tool integrations, reliable APIs, evaluation, observability, approval controls, cloud infrastructure, and maintainable backend architecture.
A production-style operations agent demonstrating how an AI system can safely interact with business data and sensitive workflows.
🔐 Prompt-injection containment
🛡️ Human approval for sensitive actions
🗄️ Isolated application and governance data
🧪 Repeatable safety and reliability evaluations
🧾 Tamper-evident audit history
A reusable governance layer between an AI agent and the tools or systems it can act on.
⚙️ Typed and risk-classified tools
📋 Policy-based execution decisions
👤 Human approval workflows
🔍 Provenance and session-risk tracking
🔗 Hash-chained audit events
🚀 Production backends for SaaS products
🤖 Custom AI workflows and tool-using agents
🔐 Secure agent integrations and approval controls
🧪 Evaluation, validation, fallback, and observability systems
☁️ AWS architecture, deployment, and reliability improvements
⚡ High-performance APIs, databases, and event-driven systems
Languages: Python, Go, Node.js
Backend: FastAPI, REST, gRPC, microservices, event-driven systems
Data: PostgreSQL, MongoDB, Redis, Kafka
Cloud: AWS, Docker, CI/CD
AI systems: LLM integrations, structured outputs, tool-using agents, evaluations, approval workflows
I design for real users, failures, security, maintainability, and future change—not only for a successful demo.
My work combines practical delivery with clear architecture, documented decisions, automated testing, and production-focused safeguards.
I work with SaaS founders and small engineering teams on production backend systems, custom AI workflows, agent reliability, and cloud architecture.