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Software Engineer, AI Systems (Canada)

AI Fund· AI Fund – Haven Safety AI
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RemoteFull-timePosted 3w ago

About the role

Role focused on AI and LLM engineering to connect Haven’s evidence base and knowledge graph to product experiences for investigators and safety leaders. Build, operate, and improve production reasoning systems, agent orchestration, grounding/retrieval, evaluation, and production AI operations. Reports to the CTO in a remote, Canada-based full-time position.

Responsibilities

  • Production reasoning systems. Build and operate multi-step LLM pipelines that coordinate model calls, tool calls, graph queries, retrieval, quality gates, and specialist-agent handoffs.
  • Agent orchestration. Extend Haven’s coordinated agent team and the orchestration layer that carries an incident from evidence through analysis, review, and enterprise learning.
  • Grounding and retrieval. Design the context layer across Neo4j graph traversal, vector search, and hybrid retrieval so every model call receives the right evidence and organizational knowledge.
  • Evaluation. Build datasets, scoring, regression suites, model comparisons, human-label loops, and per-stage quality attribution for extraction and reasoning tasks.
  • Production AI operations. Implement tracing, tool-call audits, cost and latency monitoring, failure handling, and quality dashboards; catch loops, hallucinations, and silent drift before customers do.
  • Technical direction. Select models by task across OpenAI, Anthropic, and Google; partner with product and knowledge engineering; and help shape the AI roadmap.

Qualifications

  • Production LLM systems. AI or ML engineering, including shipping LLM systems that real users depend on.
  • Agentic workflows. Hands-on experience building and debugging multi-step, tool-calling workflows with LangGraph, LangChain, or an equivalent framework.
  • Evaluation discipline. A repeatable approach to LLM evaluation, including representative datasets, regression testing, LLM-as-judge techniques, or human review loops.
  • Retrieval judgment. Experience assembling context for LLMs and a clear point of view on what to retrieve, how much, and why.
  • Production ownership. A track record of owning systems from deployment through monitoring and incident response, including a strong story about a failure or regression you diagnosed and fixed.
  • Model judgment. Comfort working across model providers and explaining tradeoffs in quality, latency, cost, context, and operational risk.
  • Graph reasoning. Comfort with Neo4j and Cypher, or a comparable graph store, and the ability to ramp quickly on graph data modeling.
  • Strong Python. Production habits around FastAPI, asynchronous services, testing, observability, and maintainable interfaces are required.

Skills

Required:Production LLM systemsAgentic workflowsEvaluation disciplineRetrieval judgmentProduction ownershipModel judgmentPython proficiency
Nice to have:Graph reasoning
Last updated Oct 21, 2026. Interested? Apply on the company site.

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