Software Engineer, AI Systems (United States)

Remote · Full-time · Remote · Posted Sep 28, 2026

At a glance

This is a build-and-operate role reporting to the CTO, focusing on the AI and LLM engineering layer that connects Haven’s evidence base and knowledge graph to product experiences.
You will design, ship, instrument, and improve reasoning systems using production evidence in a technically demanding, safety-critical environment.
As an important member of the founding team at this pre-seed venture, you will be expected to wear multiple hats.

Build and operate multi-step LLM pipelines that coordinate model calls, tool calls, graph queries, retrieval, quality gates, and specialist-agent handoffs.
Extend Haven’s coordinated agent team and the orchestration layer that carries an incident from evidence through analysis, review, and enterprise learning.
Design the context layer across Neo4j graph traversal, vector search, and hybrid retrieval.
Build datasets, scoring, regression suites, model comparisons, human-label loops, and per-stage quality attribution for extraction and reasoning tasks.
Implement tracing, tool-call audits, cost and latency monitoring, failure handling, and quality dashboards.
Select models by task across OpenAI, Anthropic, and Google.
Partner with product and knowledge engineering to help shape the AI roadmap.

Production LLM systems experience, including shipping LLM systems that real users depend on.
Hands-on experience building and debugging multi-step, tool-calling workflows with LangGraph, LangChain, or an equivalent framework.
A repeatable approach to LLM evaluation, including representative datasets, regression testing, LLM-as-judge techniques, or human review loops.
Experience assembling context for LLMs and a clear point of view on retrieval strategies.
A track record of owning systems from deployment through monitoring and incident response.
Comfort working across model providers and explaining tradeoffs in quality, latency, cost, context, and operational risk.
Strong Python skills with production habits around FastAPI, asynchronous services, testing, observability, and maintainable interfaces.

Comfort with Neo4j and Cypher, or a comparable graph store, and the ability to ramp quickly on graph data modeling.
Deep graph experience including Cypher fluency, schema evolution, MERGE patterns, embeddings, and operating a live knowledge graph.
Enterprise AI security knowledge including prompt-injection awareness, context-leak prevention, tenant isolation, role-based access, and policy-layer separation.
Experience running production AI services in Azure and using Azure AI Search, Pinecone, MongoDB Atlas, pgvector, Elasticsearch, or a similar platform.
Prior experience operating in an enterprise-level B2B SaaS environment.

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