About the role
About GeneralMind
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.
Our platform combines AI agents, operational context graphs, and real transaction data to achieve Autopilot-grade automation in real operational environments.
We work closely with leading global companies across manufacturing, retail, and logistics to deploy AI into mission-critical workflows.
About the role
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.
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.
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.
What you will do
AI Ownership
Own the agent layer end-to-end: prompt and context design, tool use, orchestration, evals, and long-term technical direction.
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.
Set the bar on how we build with LLMs: eval-driven development, context engineering, structured outputs, cost and latency discipline.
Make foundational decisions with conviction - which model, which pattern, which guardrail - document the reasoning, and evolve them as the system grows.
LLM Systems in Production
Design agents that survive real enterprise environments: ambiguous inputs, flaky upstream systems, exception cases that weren’t in anyone’s spec.
Build the eval harness that lets us ship confidently: offline evals, online evals, regression suites, human-in-the-loop review.
Think in failure modes: hallucinations, silent drift, prompt injection, tool misuse. Build the detection before you need it.
Own cost and latency as first-class product requirements, not afterthoughts.
Agent & Context Engineering
Design agent architectures that compose cleanly: tool selection, state, memory, planning, escalation.
Build the context layer that makes agents actually useful on top of SAP and Oracle data - retrieval, grounding, schema awareness, auditability.
Evolve agents without breaking live customer deployments; manage prompt and model versioning with intention.
Know when the right answer is a bigger model, a better prompt, a fine-tune, a tool, or just classical code.
Reliability & Systems Thinking
Detect regressions in agent behavior before a customer does; build the evals and observability to catch them early.
Own the operational posture of the AI systems you ship: alerting, runbooks, replay, on-call readiness.
Identify structural debt in prompts, pipelines, and context before it compounds; refactor with thought.
What we are looking for
Experience: 6+ years building production software, with clear ownership of LLM-powered systems in real customer environments - not demos, not notebooks.
LLM depth: Non-negotiable. You’ve shipped agents or LLM features that run on live traffic. You have strong opinions about prompting, context, tool use, structured outputs, and what breaks in production.
Evals as a first instinct: You don’t ship LLM code without a way to measure it. You’ve built eval pipelines that actually catch regressions, not just vibes.
Python depth: You’re fluent in Python and comfortable in production service codebases. You make deliberate calls about async, framework choice, and performance.
Reliability instinct: Hallucination containment, idempotency on tool calls, graceful degradation when a model is wrong - part of how you design, not patched in later.
End-to-end ownership: You’re comfortable being the person who holds the full agent picture - from prompt to production incident. You identify what’s broken or missing before you’re asked.
A real spike: We’re a strong team with broad coverage. What we’re hiring for is someone who brings a 10x improvement in an area we’re currently underinvested in. That might be agent evaluation, context engineering on enterprise data, agentic reliability, deep NLP/LLM internals, or domain-specific grounding. We don’t know exactly what shape it takes - but you should know what yours is, and be able to articulate why it matters.
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