Applied AI Engineer (Agentic Systems)

Role overview

As an Applied AI Engineer (Agentic Systems) at ShipBob, you will lead the transition from manual workflows to autonomous agentic operations. You’ll architect and implement agents that can reason, call tools, and execute complex business logic—with an emphasis on reliability in logistics.

Key missions

• Architect & build autonomous agents for multi-step reasoning, tool-calling, and error recovery.
• Embed with logistics and support teams to identify high-impact opportunities for agentic intervention.
• Own the entire agent lifecycle, from design to deployment and ongoing improvement.
• Develop guardrails, evaluation frameworks, and logging systems so agents behave predictably in a high-stakes supply chain environment.
• Contribute to the “Agent Hub” and help bridge the gap between prototypes and production-ready tools.

What you’ll bring

• A builder mindset with high agency, comfort in ambiguity, and obsession with moving from “software that waits” to agents that take action.
• Systems thinking experience, including retrieval pipelines (RAG), vector databases, and clean APIs for agent consumption.
• Engineering rigor with 1–3 years software engineering experience (or a standout portfolio), strong in Python and .NET.
• LLM proficiency, with hands-on experience using LLM APIs (OpenAI, Anthropic) and turning model output into structured, deterministic actions.
• Agentic expertise in modern agent architectures (e.g., ReAct, Plan-and-Execute) and orchestration frameworks (e.g., LangGraph, CrewAI, Semantic Kernel).
• A bias for action: prototype quickly and challenge assumptions to deliver outcomes.
• Full-stack mindset to build lightweight dashboards/interfaces for monitoring and human interaction (Vue.js/React).

Nice-to-haves / additional notes

• A strong portfolio of agentic projects is acceptable in place of years of experience.
• People are expected to roll up their sleeves and tackle complex supply-chain problems.