AI-native Staff Backend Engineer

You can use agentic workflows (Claude Code, Cursor Agent, or equivalent) as your default, not as a novelty.

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.

You understand model routing trade-offs (e.g., when to use Haiku vs. Sonnet vs. Opus) and multi-agent orchestration.

Strong ownership instinct: AI output is a first draft, not a deliverable; you validate, refactor, and take full accountability.

You have strong Coding and Computer Science Fundamentals. Roughly ⅔ of your time will be hands-on technical work: designing solutions, steering and reviewing AI-generated code, developing new ideas, and reviewing others' contributions. The rest goes to cross-team influence, mentoring, and strategic alignment.

Strong background experience in Golang, Java/Kotlin (preferred technologies), and able to work with PHP. Flexible and capable of adjusting to changing project requirements, roles, and backend technologies as needed.

Scalable Systems Expertise. Experience with large-scale web applications, an economic-thinking mindset, and being able to design well-bounded future-proof domain systems, aligned with the business objectives.

Deep expertise in scalable distributed systems, CI/CD, RESTful and GraphQL APIs, AWS, and relational + NoSQL databases.

Big-picture thinker: aligns technical decisions with business strategy, influences without authority.

Clear communicator in English across engineering, product, and leadership audiences.

Self-starter who identifies what needs doing and takes initiative without waiting to be asked.

Skilled Technical Leader and Mentor: Guide and support engineers by setting a strong example. As a Staff Engineer, you are a role model, leading by example and through your expertise.

Architect context pipelines for AI agents: curate what enters the context window (repo boundaries, domain knowledge, test outputs, CI feedback) so agents produce reliable, mergeable code, not just plausible code.

Prepare task specifications that are agent-actionable (clear acceptance criteria, referenced modules, explicit constraints)

Design and maintain agent-ready systems: well-bounded repos, comprehensive test suites as guardrails, CI/CD pipelines with structured output agents can parse and act on.

Serve as an AI Champion: join and strengthen our growing AI Champion community, contribute to workshops, curate effective agentic workflows, and mentor engineers on AI-assisted practices. Contribute to org-level AI tooling evaluations and OLX AI practices repositories.

Drive active migration from a legacy monolith to microservices, owning decisions on bounded domain decomposition. Working on making agentic workflows effective across a codebase whose boundaries are actively being redefined.

Set the bar for GraphQL API design, RESTful contracts, database schema, and CI/CD quality across the tribe.

Work with product and engineering leadership to identify high-value problems, propose initiatives, and drive impactful technical direction without direct authority

Guide senior engineers in managing and reducing technical debt with AI as an accelerator, not a shortcut.

Recruiter Interview 

Head of Engineering Interview

Live Coding

System Design