Senior Back-end Engineer

Company Description Cooper is an AI-driven enterprise decision-making platform that empowers executives to make critical daily decisions with greater accuracy and efficiency. Cooper helps organizations improve revenue, EBITDA, and cash flow while fostering agility in competitive markets. Its intelligent platform tracks decision performance, promoting continuous improvement and strategic growth. With Cooper, businesses can achieve exceptional outcomes and position themselves as market leaders. Role Description This is a full-time remote role for a Senior Back-end Engineer. The Senior Back-end Engineer will design, develop, and optimize scalable back-end systems, which is a sophisticated multi-agent system that orchestrates LLM agents over real business data plus a streaming chat surface that customers use every day. Our stack includes AWS, Snowflake,Airbyte, FastAPI, Tortoise ORM, Pydantic AI, LLM Providers (Anthropic, OpenAI, etc), and React. What you’ll do Own every architectural decision on the backend and set the engineering patterns the rest of the team follows Implement features end-to-end across the backend, data layer, and LLM orchestration Debug production incidents that span timing, state, and multiple subsystems and ship precise, targeted fixes Translate ambiguous business requirements into clean, defensible implementations Stay current on agentic systems development including multi-agent orchestration, tool and function-call design, context engineering, and evaluating non-deterministic behavior in production Sequence changes safely across migrations, feature flags, rollout plans, observability hooks, and kill switches Use AI coding tools effectively and know when to override them Qualifications 8+ years of backend engineering experience, with a track record of owning architectural decisions end-to-end Deep experience with Python and modern backend frameworks (FastAPI, async ORMs, Pydantic) Hands-on experience building and operating LLM-powered or agentic systems in production (multi-agent orchestration, tool/function-call design, prompt engineering, structured outputs, retrieval) Proficiency with cloud data infrastructure including AWS, Snowflake, or equivalent Strong debugging instincts: able to diagnose issues that span timing, state, and multiple subsystems without hand-holding Ability to translate ambiguous product requirements into defensible technical implementations and push back before building when the spec is wrong Experience shipping continuously with safety in mind: migrations, feature flags, rollout plans, observability, and kill switches Comfortable reasoning about AI model tradeoffs across cost, latency, determinism, retrieval strategy, and model selection Skilled at leveraging AI coding tools (Claude, Cursor, Codex, etc.) effectively, including knowing when not to trust them Strong written communication across proposals, implementation plans, PR descriptions, and incident retros that future engineers can follow without follow-up questions #J-18808-Ljbffr Cooper