You use agentic workflows (Claude Code, Cursor Agent, or equivalent) as your default, not as a novelty — you understand model routing trade-offs (e.g., when to use Haiku vs. Sonnet vs. Opus) and multi-agent orchestration.

You practice context engineering, not just prompt engineering — you design the full information architecture an AI agent needs to succeed: repo structure, CLAUDE.md / AGENTS.md configs, relevant code context, memory, and tool access.

Have strong CS fundamentals and hands-on technical depth in Golang, Java (Kotlin) (preferred technologies), able to work with PHP — you spend the majority of your time designing solutions, steering and reviewing AI-generated code, and contributing to a large-scale codebase.

You have scalable systems expertise: experience with large-scale web applications, well-bounded domain design, AWS cloud architecture, IaC (Terraform, CloudFormation), and solid database fluency (MySQL, DynamoDB, Redis, Memcached).

You are a product-minded engineer: you challenge decisions with a cost/value lens, articulate how services should evolve to meet business goals, and communicate clearly in English across engineering and product audiences.

You live "you build it, you run it, you own it" — AI output is a first draft, not a deliverable. You validate, refactor, take full accountability, and proactively identify what needs to be done without waiting to be asked.

Apply context pipeline discipline within your team's codebase: scope context windows to repo boundaries, domain knowledge, test outputs, and CI feedback so agents consistently produce reliable, mergeable code on real team work items.

Write agent-actionable task specifications for your team's backlog (clear acceptance criteria, referenced modules, explicit constraints) and keep the codebase agent-ready: well-bounded repos, comprehensive test suites as guardrails, and CI/CD pipelines with structured output agents can parse and act on.

Lead delivery of complex initiatives: break them into incremental milestones, manage cross-team dependencies, navigate ambiguity, and make deliberate quality-vs-delivery trade-offs — ensuring timely delivery without sacrificing standards.

Mentor engineers and contribute to the AI Champion community: share effective agentic workflows, run workshops, and help the team adopt AI-assisted practices.

Uphold standards for GraphQL API design, RESTful contracts, database schema, and CI/CD quality; lead incident response for your team; and manage technical debt with AI as an accelerator, not a shortcut.