AI Engineer

We are looking for engineers excited about building long-lived AI systems, not chatbots. As a Founding AI Systems Engineer, you own the AI core of the platform: agents, tools, ontology generation, memory, retrieval, and evaluation. This is not a model-training role. The work is agent architectures, semantic retrieval, knowledge representation, enterprise data systems, decision intelligence, and autonomous learning from operational exhaust: the workflow steps, approvals, log entries, and data changes an enterprise produces as it runs.

Above all, you build in an LLM-first, reasoning-first way. Nearly everything you ship should make the platform a little more self-improving, the way Anthropic let Claude Code help write Claude Code.

About the Role

• LLM orchestration and tool calling: the frameworks StarLifter’s agents use to reason over enterprise context and act.

• Agent frameworks: agentic systems that observe signals, reason about decision patterns, and recommend or automate governed actions.

• Ontology generation: pipelines that induce a machine-readable model of each enterprise (its products, customers, orders, rules, and policies) from its own systems and knowledge bases.

• Embedding pipelines and knowledge graph integration: the semantic retrieval layer that serves enterprise behavior back to agents.

• Operational memory: the durable record of events, decisions, actions, and outcomes that lets the platform learn from every decision.

• Evaluation harnesses: the systems that score confidence, benchmark outcomes, and prove decision quality improves over time.

Requirements

• Real depth in modern AI tooling: LLM orchestration, RAG architectures, retrieval systems, and knowledge graphs in production. This field is young (these tools are only a couple of years old), so we care more about how far you’ve pushed them, and how fast you learn, than about years on a résumé.

• Hands-on with agent frameworks and orchestration tooling such as LangGraph and DSPy.

• Strong engineering fundamentals and comfortable in a modern typed stack (we build in TypeScript + Node). You write production-quality code and think about reliability and scale by default.

• Genuinely excited by enterprise ontology induction, operational memory, decision patterns, agentic systems, and learning from operational exhaust.

• The kind of engineer who tests assumptions and shows up knowing more than we do about deep AI. You’ll challenge our thinking and make the platform better for it.

• Intentional about your work and your career: you build meaningful, long-lived systems and stay to see them through, energized by the ownership and ambiguity of an early-stage company.

• A craftsperson and a colleague: low ego, high standards, excited to build alongside a small, senior, highly collegial team.

Nice to have:

• Working familiarity with core enterprise business processes (Quote-to-Cash, Procure-to-Pay, Hire-to-Retire) and ERP data, enough to understand what the platform is reasoning about.

• Semantic modeling, metadata systems, or business-context modeling experience.

• Deep familiarity with enterprise integrations (SAP, Oracle, ServiceNow, Salesforce, Snowflake, Databricks).

• Prior founding-engineer or very-early-startup experience.