OnHires - AI Product Engineer
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Requirements
• Strong product instincts — you simplify complexity, identify user pain, and focus on what matters. • Strong builder mindset — you prototype first, refine later. • AI-native development experience — hands-on with tools like Claude Code, Codex, or similar. • TECHNICAL & AI EXPERTISE • Deep experience with AI coding agents: • Task decomposition, context management, output review, and iteration • Multi-file changes, tool usage, and workflow orchestration • Solid understanding of agentic systems, including: • You don’t just generate code—you: • Review, test, and refine it • Structure it for maintainability • Know when to move fast—and when to slow down • You understand the difference between quick experiments and systems that can scale—and you know how to build both. • Experience building AI assistants, copilots, or agent-based systems • Familiarity with evals, retrieval, tracing, and observability • Experience with product analytics and experimentation • Background in e-commerce, edtech, fintech, or trading systems • Experience building internal tools or zero-to-one products • Exposure to regulated or high-stakes environments • HOW SUCCESS WILL BE MEASURED (FIRST 90 DAYS) 📈 • Ship multiple high-quality prototypes and internal tools • Significantly reduce time from idea → testable product • Demonstrate strong judgment in: • When to use AI agents • When to simplify • When to productionize • Create reusable patterns for AI-assisted product development • Contribute meaningfully across multiple product areas
Responsibilities
• Turn ambiguity into execution • Translate rough ideas into clear product concepts, flows, and working prototypes. • Build fast with AI agents • Use tools like Claude Code, Codex, and similar workflows to ship MVPs, experiments, and production components quickly. • Design AI-native products • Create experiences where AI is core to the product—not just an add-on. • Develop agent workflows • Design systems that combine prompts, tools, retrieval, memory, and orchestration patterns effectively. • Bridge prototype → production • Know when something is “good enough” to validate—and when it needs to be hardened for scale. • Collaborate cross-functionally • Work closely with product, design, and engineering to prioritize high-impact problems and ship quickly. • Build across the stack • Contribute to frontend, backend, integrations, internal tools, and automation as needed. • Ensure reliability • Implement lightweight evals, QA checks, and monitoring for AI-driven systems. • Document and scale learnings • Capture insights, reduce handoff friction, and turn experiments into repeatable patterns.
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