Now

I am focused on shipping practical automation and AI systems that survive real production constraints: reliability, observability, and clear rollback paths.

What I'm building

• Production-grade agent workflows with strict tool boundaries and permission checks.
• CI-first evaluation pipelines for LLM features, including regression gates before deploy.
• Reusable project templates that cut setup time for new automation use cases.

What I'm learning

• Better model routing strategies across latency and cost tiers.
• Hardening retrieval systems with citation discipline and failure-mode testing.
• Clean deployment patterns for local-first AI stacks in enterprise contexts.

What I'm open to

• Python automation and platform engineering roles with strong ownership.
• Applied AI engineering work focused on reliable shipping, not demos.
• Consulting projects where teams need to stabilize and productionize agent systems.