Software Engineer, AI Systems

Location: United States; remote. Relocation will not be considered.

Company

hirify.global is a VC-backed pre-seed venture building an enterprise learning intelligence layer for safety-critical organizations in partnership with The AES Corporation and AI Fund.

What you will do

• Build and operate multi-step LLM reasoning pipelines with model calls, tool calls, graph queries, retrieval, quality gates, and specialist-agent handoffs.
• Extend the coordinated agent orchestration layer supporting incident investigation, review, causal analysis, and enterprise learning.
• Design grounding and retrieval across Neo4j graph traversal, vector search, hybrid retrieval, company knowledge, and historical cases.
• 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 for production AI systems.
• Select and compare models from OpenAI, Anthropic, and Google while shaping the AI roadmap with product and knowledge engineering.

Requirements

• Production AI or ML engineering experience, including shipping LLM systems used by real users.
• Hands-on experience building and debugging multi-step, tool-calling workflows with LangGraph, LangChain, or an equivalent framework.
• Experience with repeatable LLM evaluation using representative datasets, regression testing, LLM-as-judge methods, or human review loops.
• Experience assembling retrieval context for LLMs and making informed decisions about evidence selection and context size.
• Ownership of deployed systems through monitoring and incident response, including diagnosing and fixing failures or regressions.
• Strong Python production skills, including FastAPI, asynchronous services, testing, observability, and maintainable interfaces; Neo4j and Cypher or a comparable graph store are strongly preferred.

Nice to have

• Deep graph experience with Cypher, schema evolution, MERGE patterns, embeddings, and live knowledge graph operations.
• Experience with enterprise AI security, including prompt-injection awareness, context-leak prevention, tenant isolation, role-based access, and policy-layer separation.
• Experience with Azure, Azure AI Search, Pinecone, MongoDB Atlas, pgvector, Elasticsearch, or similar search platforms.
• Prior experience operating B2B enterprise SaaS systems.

Culture & Benefits

• Work on evidence, causal pathways, controls, organizational history, and corrective actions in a safety-critical domain.
• Evaluation-first culture focused on measurable, observable, and improvable quality.
• Direct customer impact through products used by safety teams in energy, utilities, infrastructure, construction, and manufacturing.
• Small founding team with high ownership and close collaboration with the CTO, product, and knowledge engineering.

Hiring process

• The role reports to the CTO and involves close collaboration with product and knowledge engineering.
• No agency inquiries; sponsorship is not provided.