AI Harness Engineer - Agentic & Autonomous Operations (m/f/x)

xHeron is building the Service-as-Software company for short-term rental operators. We take responsibility for guest communication, team coordination and issue resolution, so customers can hand over the work without handing over their business. Today, we deliver that service through people, software and AI. We’re turning the expertise behind it into autonomous systems that understand situations, take action and follow through. Not another tool to supervise. A service that gets the job done. We’re looking for an AI Engineer to build the agentic capabilities at the heart of xHeron. You’ll help shape how our agents reason, act and learn, and build a company where AI doesn’t just support the work, but increasingly delivers it. Tasks We are looking for an AI Harness Engineer to build the software layer that turns language models into dependable operational systems. You will work on orchestration, context assembly, tool execution, state, permissions, evaluations, observability, failure recovery, and human handoffs. The goal is not merely to generate a good response. What You’ll Own Agent harness architecture: Build the control layer around models: task decomposition, state management, model and tool selection, execution loops, checkpoints, retries, stopping conditions, and human handoffs. Decision and execution behavior: Design how agents interpret situations, choose actions, manage uncertainty, and complete bounded operational tasks—not only how they generate text. Context and memory: Determine what an agent needs to know at each step. Build context assembly, retrieval, memory, evidence selection, and knowledge-maintenance strategies while managing relevance, latency, and cost. Tools and real-world effects: Connect agents to APIs and operational systems through explicit tool contracts, permissions, validation, idempotency, and confirmation that the intended effect actually occurred. Evaluation and regression testing: Build representative evaluation suites and automated tests for decision quality, tool use, task completion, safety, escalation behavior, and recurring failure patterns. Observability and failure analysis: Make agent behavior inspectable through structured traces, state, outcomes, and error classification. Diagnose failures across models, prompts, context, tools, and surrounding software. Continuous improvement: Use evaluations, production traces, operator feedback, and controlled experiments to improve prompts, context, tools, model choices, and agent architecture. Production ownership: Ship and operate production Python software alongside our CTO and engineering team. Work closely with systems engineering on integrations, authorization, durable state, recovery, and safe execution. Requirements What Makes You a Great Fit You are a strong software engineer who can design, build, test, and operate reliable production systems in Python. You have built a substantial LLM-powered, automation, workflow, or decision system: not only notebooks, chat interfaces, prompt experiments, or a basic retrieval pipeline. You understand the engineering layer around a model: orchestration, state, context, tools, structured outputs, permissions, retries, fallbacks, observability, and evaluations. You can connect intelligence to real work: selecting relevant evidence, making a bounded decision, invoking tools or APIs, and verifying that the intended result occurred. You design for partial failure and uncertainty. You think deliberately about when an agent should act, retry, wait, stop, ask for help, or escalate. You evaluate behavior systematically. You use representative cases, traces, outcome checks, comparisons, and regression tests rather than treating a plausible response as proof of success. You have shipped software that people or operational processes depend on and have worked through debugging, timeouts, inconsistent data, changing APIs, and production incidents. You can inv