Build AI-native product features
Design and evolve LLM-powered workflows and agentic systems that solve real customer problems.
Build multi-step agents with state, memory, tool use, routing, and evaluation.
Turn fuzzy requirements into working systems through fast iteration and feedback.
Own features end-to-end: design, implementation, iteration, and reliability.
Ship like a product engineer
Collaborate closely on product direction and tradeoffs , not just implementation.
Think in outcomes, user value, and constraints-not just architecture diagrams.
Consider cost, latency, observability, and failure modes from day one.
Use AI to accelerate development itself: prototyping, refactoring, testing, evaluation.

Use Python as a thinking tool , not just a language
comfortable turning vague ideas into working systems and iterating quickly.
Enjoy tackling messy, ill-defined problems and shaping them into clear, testable solutions.
Have built LLMs in practice and understand their strengths, limits, and failure modes .
Are curious about agentic systems and workflows
you experiment, prototype, and refine rather than wait for “best practices.”
Think in end-to-end systems : inputs → reasoning → tools → outputs → feedback.
Care about outcomes and leverage, not just correctness or architectural purity.
Have a bias toward automation and eliminating manual work when software can do it better.
Are comfortable shipping imperfect versions, learning from real usage, and improving fast.
Naturally think about reliability, cost, and performance once something starts to matter.

Software Product Engineer – Agentic, AI Systems

Key skills