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AI Fund
AI Fund
11 – 50 Employees
ConsultingHealthcareManufacturing
AI Fund is a venture studio that helps launch and develop companies built around artificial intelligence. Working with founders and technology pioneers, it takes a minority co-founder role and supports the creation of AI applications across fields including manufacturing, mental health, education, and healthcare. Its work combines venture building with practical support for teams applying AI to challenges in diverse industries.
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AI Systems Software Engineer - AI Fund - Canada Remote

Build production LLM pipelines and agent orchestration for Haven Safety AI’s enterprise safety intelligence platform. Improve retrieval, evaluation, observability, and model quality for regulated industries.

CACanadaRemoteFull-timeMiddleSeniorEnglish RequiredAI EngineerMachine Learning Engineer
AzureElasticSearchMongoDBNeo4jPython

Description

  • Develop and operate multi-step LLM pipelines that coordinate model and tool calls, graph queries, retrieval, quality gates, and specialist-agent handoffs.
  • Expand Haven’s coordinated agent team and orchestration layer from incident evidence through analysis, review, and enterprise learning.
  • Design context systems using Neo4j graph traversal, vector search, and hybrid retrieval.
  • Create evaluation datasets, scoring methods, regression suites, model comparisons, human-review loops, and stage-level quality attribution.
  • Add tracing, tool-call auditing, cost and latency monitoring, failure handling, and quality dashboards.
  • Identify orchestration loops, hallucinations, and undetected model drift before they affect customers.
  • Choose among OpenAI, Anthropic, and Google models according to task requirements.
  • Collaborate with product and knowledge engineering while helping define the AI roadmap.
  • Report to the CTO and contribute across a range of founding-team responsibilities.

Requirements

  • Professional AI or machine learning engineering experience, including delivery of LLM systems used by real customers.
  • Practical experience building and troubleshooting multi-step, tool-calling workflows with LangGraph, LangChain, or an equivalent framework.
  • A repeatable LLM evaluation process using representative datasets, regression tests, LLM-as-judge methods, or human-review loops.
  • Experience preparing LLM context and making informed decisions about what to retrieve, how much to retrieve, and why.
  • Demonstrated ownership of systems through deployment, monitoring, and incident response.
  • Experience investigating and resolving a production failure or regression.
  • Ability to work across model providers and explain tradeoffs involving quality, latency, cost, context, and operational risk.
  • Working knowledge of Neo4j and Cypher, or a comparable graph database, with the ability to learn graph data modeling quickly; strongly preferred.
  • Strong Python development skills.
  • Production experience with FastAPI, asynchronous services, testing, observability, and maintainable interfaces.
  • Advanced graph experience, including Cypher, schema evolution, MERGE patterns, embeddings, and operation of a live knowledge graph; nice to have.
  • Experience with enterprise AI security; nice to have.
  • Experience with Azure and hybrid search; nice to have.
  • Experience with Azure AI Search, Pinecone, MongoDB Atlas, pgvector, Elasticsearch, or similar platforms; nice to have.
  • Previous enterprise B2B SaaS experience; strongly preferred.

Benefits

  • Join a small team with substantial ownership.
  • Receive direct feedback from safety teams and see the effect of your work with customers.
  • Work closely with the CTO, product, and knowledge engineering teams.
  • Influence consequential technical decisions and deliver changes quickly to customers.
  • Tackle complex reasoning challenges in a safety-critical domain.
  • Work in an evaluation-first culture centered on measurable, observable, and improvable quality.

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