Software Engineer, AI Systems

🔥 12 hours ago

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

👻 Ghost score 22%

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Logo of AI Fund

AI Fund

11 - 50 employees

Founded 2017

💼 Consulting

🏥 Healthcare

🏭 Manufacturing

Consulting • Healthcare • Manufacturing

AI Fund is a venture studio focused on launching AI-based companies that aim to drive progress across various sectors. By partnering with talented founders and tech pioneers, AI Fund supports the development of innovative applications of artificial intelligence, spanning industries such as manufacturing, mental health, education, and more. The organization acts as a minor co-founder, helping to cultivate startups that leverage the transformative potential of AI to improve the world.

📋 Description

• Build and operate multi-step LLM pipelines coordinating model calls, tool calls, graph queries, retrieval, quality gates, and specialist-agent handoffs • Extend Haven’s coordinated agent team and orchestration layer from incident evidence through analysis, review, and enterprise learning • Design the context layer across Neo4j graph traversal, vector search, and hybrid retrieval • Build evaluation datasets, scoring systems, 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 • Detect loops, hallucinations, and silent drift before customers do • Select models across OpenAI, Anthropic, and Google based on task requirements • Partner with product and knowledge engineering • Help shape the AI roadmap • Design, ship, instrument, and improve AI/LLM reasoning systems using production evidence • Report to the CTO

🎯 Requirements

• AI or ML engineering 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 • 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 what to retrieve, how much, and why • Track record of owning systems from deployment through monitoring and incident response, including diagnosing and fixing a failure or regression • Comfort working across model providers and explaining tradeoffs in quality, latency, cost, context, and operational risk • Comfort with Neo4j and Cypher, or a comparable graph store, and ability to ramp quickly on graph data modeling (strongly preferred) • Strong Python • Production experience with FastAPI, asynchronous services, testing, observability, and maintainable interfaces • Cypher fluency, schema evolution, MERGE patterns, embeddings, and operating a live knowledge graph (nice to have) • Prompt-injection awareness, context-leak prevention, tenant isolation, role-based access, and policy-layer separation (nice to have) • Experience running production AI services in Azure and using Azure AI Search, Pinecone, MongoDB Atlas, pgvector, Elasticsearch, or a similar platform (nice to have) • Prior experience operating in an enterprise-level environment (strongly preferred) • Must be based in the US; relocation will not be considered

🏖️ Benefits

• Meaningful reasoning problems • An evaluation-first culture • Visible customer impact • Small team, high ownership • Direct feedback from safety teams and users • Opportunity to work closely with the CTO, product, and knowledge engineering • Opportunity to make consequential technical decisions and see work reach customers quickly

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