paretos

• As a Staff AI Backend Engineer, you own the technical backbone of our Decision Intelligence platform and the architecture that lets it scale. • You set the direction for how we build scalable backend services that bring our forecasting and optimization models, agentic workflows, and our semantic data layer into productive use. • You work AI-first: Claude Code or similar CLIs, MCPs, and custom skills are your standard toolkit, and you define how the rest of the team uses them. • You think not only in terms of endpoints and databases, but in customer value, product impact, and adoption - and you are relentless about strengthening the system, not just solving the task at hand. • With a strong product mindset, you set technical direction: defining how we measure feature adoption, instrumenting usage patterns, and steering the roadmap with Product and FDE — accountable to outcomes, not output. • Scalable backend services in Python (FastAPI, Pydantic) for agent workflows, forecasting, and replenishment are yours to architect, build, and operate — including the decisions, trade-offs, and standards behind them. • Clean API and service contracts between platform, agents, ML models, frontend, and customer systems (ERP, BI, planning tools) are designed by you, with robustness, observability, and reliability as first-class properties. • Our semantic data layer — the foundation for accurate forecasts and precise agent responses — is yours: PostgreSQL modeling, dbt transformations, pipelines, caching, and retrieval, built to scale across customers and use cases. • How we integrate LLM and agent systems into the backend is defined by you: tool use, function calling, RAG pipelines, eval frameworks, guardrails, and production deployment — setting the patterns and quality bars the team builds against. • Driving our AI-first engineering culture is central: Claude Code, AI-assisted development, and subagents are part of your daily toolkit, and you evolve our internal setup (CLAUDE, hooks, skills, plan/execute/review loop, plugins, docs) so the team ships faster and safer. • Ambiguous business and customer requirements get translated into coherent technical strategy with FDEs and Product, making pragmatic trade-offs between speed, scalability, and maintainability — explained clearly to technical and non-technical stakeholders. • Prototyping fast and close to the user, you put ideas into FDEs' and customers' hands in days, gather feedback autonomously, iterate on the real workflow, and decide from actual usage what to harden and roll out. • Through code and design review, mentoring, documentation, and standards, you raise the engineering bar — growing the people around you and making the system more resilient over time.