Senior AI Engineer (all genders)

<h2><strong>About GeneralMind</strong></h2><p>GeneralMind builds AI Systems of Action for complex enterprise workflows. We deploy AI employees that automate messy operational handovers across supply chains - processing orders, validating invoices, coordinating suppliers, and handling exceptions directly on top of enterprise systems like SAP, Oracle, JDE, and Dynamics.</p><p>Our platform combines AI agents, operational context graphs, and real transaction data to achieve Autopilot-grade automation in real operational environments.</p><p>We work closely with leading global companies across manufacturing, retail, and logistics to deploy AI into mission-critical workflows.</p><h2><strong>About the role</strong></h2><p>We are looking for a Senior AI Engineer who treats LLMs as production software, not as magic. Someone who can own the agent layer of our platform end to end - from prompt and context design, through tool use and orchestration, to evals, reliability, and continuous improvement in live customer environments.</p><p>This role sits at the intersection of two things that are rarely mastered together: shipping LLM-powered systems that actually work in messy enterprise reality, and the discipline of a real engineer - evals, observability, cost, latency, failure modes. Models get smarter every month. What stays scarce is the engineering judgment to make them reliably useful on top of SAP, Oracle, and the mess of real operational data.</p><p>You will be a senior individual contributor working directly with the founders. The person in this role won’t be handed a roadmap - they’ll help define it, and build the AI practice around it.</p><h2><strong>What you will do</strong></h2><p><strong>AI Ownership</strong></p><ul><li><p>Own the agent layer end-to-end: prompt and context design, tool use, orchestration, evals, and long-term technical direction.</p></li><li><p>Be the person who understands why an agent made the decision it made - and who other engineers come to when a model behaves in ways they can’t explain.</p></li><li><p>Set the bar on how we build with LLMs: eval-driven development, context engineering, structured outputs, cost and latency discipline.</p></li><li><p>Make foundational decisions with conviction - which model, which pattern, which guardrail - document the reasoning, and evolve them as the system grows.</p></li></ul><p><strong>LLM Systems in Production</strong></p><ul><li><p>Design agents that survive real enterprise environments: ambiguous inputs, flaky upstream systems, exception cases that weren’t in anyone’s spec.</p></li><li><p>Build the eval harness that lets us ship confidently: offline evals, online evals, regression suites, human-in-the-loop review.</p></li><li><p>Think in failure modes: hallucinations, silent drift, prompt injection, tool misuse. Build the detection before you need it.</p></li><li><p>Own cost and latency as first-class product requirements, not afterthoughts.</p></li></ul><p><strong>Agent & Context Engineering</strong></p><ul><li><p>Design agent architectures that compose cleanly: tool selection, state, memory, planning, escalation.</p></li><li><p>Build the context layer that makes agents actually useful on top of SAP and Oracle data - retrieval, grounding, schema awareness, auditability.</p></li><li><p>Evolve agents without breaking live customer deployments; manage prompt and model versioning with intention.</p></li><li><p>Know when the right answer is a bigger model