Software Engineer, AI Systems
New
H
Haven Safety AISafety AI
Listing location: Canada; Workplace type: Remote; Structured job location: CanadaFull-Time
Salary not disclosed
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Job Details
- Required Skills
- PythonFastAPILangChain
Requirements
- Have AI or ML engineering experience, including shipping LLM systems that real users depend on.
- Have hands-on experience building and debugging multi-step, tool-calling workflows with LangGraph, LangChain, or an equivalent framework.
- Use a repeatable approach to LLM evaluation, such as representative datasets, regression testing, LLM-as-judge techniques, or human review loops.
- Have experience assembling context for LLMs and deciding what to retrieve, how much, and why.
- Have owned systems from deployment through monitoring and incident response, including diagnosing and fixing a failure or regression.
- Be comfortable working across model providers and explaining tradeoffs in quality, latency, cost, context, and operational risk.
- Have strong Python production skills, including FastAPI, asynchronous services, testing, observability, and maintainable interfaces.
- Be comfortable with Neo4j and Cypher or a comparable graph store, and able to ramp up on graph data modeling.
- Deep graph experience, including Cypher, schema evolution, MERGE patterns, embeddings, and live knowledge graphs, is preferred.
- Experience with enterprise AI security, Azure production AI services, or similar search and vector platforms is a plus.
Responsibilities
- Build and operate multi-step LLM pipelines coordinating model calls, tool calls, graph queries, retrieval, quality gates, and specialist-agent handoffs.
- Extend the coordinated agent team and orchestration layer for incident analysis, review, and enterprise learning.
- Design context retrieval across Neo4j graph traversal, vector search, and hybrid retrieval.
- Build evaluation datasets, scoring, regression suites, model comparisons, human-label loops, and per-stage quality attribution.
- Implement tracing, tool-call audits, cost and latency monitoring, failure handling, and quality dashboards.
- Select models across OpenAI, Anthropic, and Google based on task requirements.
- Partner with product and knowledge engineering and help shape the AI roadmap.
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