Staff Software Engineer

Role summary The Staff Software Engineer (IC4) on Sales and Order Management designs, builds, ships, and operates capabilities whose core behavior is model-driven rather than explicitly authored—agentic and conversational experiences that interpret a seller's or customer's intent, reason over catalog, quote, and order context, invoke tools, and act on the user's behalf across the quote-to-order lifecycle. This is not a machine learning or AI research role; the engineer does not train foundation models. It is also distinct from traditional full-stack engineering, where systems follow deterministic logic rather than selecting execution paths at runtime. Two consequences shape the work. First, the most important logic often lives in natural language—instructions, prompts, tool descriptions, guardrails, escalation rules—which must be engineered, versioned, and reviewed with the same discipline as code. Second, because behavior is probabilistic, correctness is established by measuring behavior at scale rather than by asserting fixed outputs, making automated evaluation a first-class engineering activity rather than a quality-assurance afterthought. The stakes in this domain sharpen both points. An agent operating on quotes and orders touches pricing integrity, contractual commitment, and revenue recognition—a confidently wrong output is not a bad answer, it is a mispriced order. At IC4 the engineer owns AI design decisions across the domain, not within a single feature, and owns the correctness of what ships whether a person or an agent produced it. What you do Build AI-native capability across the order lifecycle Design and ship features built around agentic behavior—intent interpretation, multi-step reasoning, tool invocation, and action on the user's behalf—together with the data models, integrations, and channels that make them usable in production. In Order Management this spans guided offer selection and configuration, natural-language order construction and validation, conversational order status and in-flight change, order-exception triage and resolution, and promise-date and availability inquiry. Design AI-driven autonomous workflows Decompose sales and order processes into the steps and decision points an agent can execute—determining where autonomy is appropriate, where a checkpoint with a person is required, and how exceptions, retries, and hand-back are handled. The design must make that distinction structural rather than advisory. Author and maintain agentic instructions as engineering artifacts Write, structure, and version the system instructions, role definitions, tool descriptions, guardrails, and escalation paths that govern agent behavior in the domain—under code review, source control, and regression coverage. Own the shared instruction and tool-description surface that adjacent teams build against. Build automated evaluation and test non-deterministic behavior Design and operate the evaluation that makes change safe — golden datasets, multi-turn conversation suites, model-as-judge scoring calibrated to human review, CI gates, and drift detection — plus adversarial, jailbreak, grounding, and tool-selection testing. Design conversational experiences across channels Build experiences that hold context across turns, hand off cleanly between automated and live agents, and behave consistently across chat and voice—accounting for what voice imposes: latency budgets, barge-in, speech recognition error, disambiguation, and explicit confirmation before consequential actions. Specify precisely and direct AI coding agents Convert requirements into testable specifications with explicit scope, constraints, non-goals, and acceptance criteria; decompose work into agent-sized tasks; supervise several workstreams in parallel; and review agent output for correctness, spec adherence, security, and maintainability. You own the result regardless of what produced it. Own quality, safety, and reliability in production Monitor convers