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Digital Iron · 21 hours ago

AI Backend Engineer - Data & Integration

United States

Full-time

Remote

Senior Level, Lead/Staff

Digital Iron is building intelligent infrastructure for predictive maintenance and parts procurement automation in the heavy equipment ecosystem. The AI Backend Engineer will design integration architecture, build bi-directional integrations, and develop knowledge graph systems to support various partnership models and enhance data accuracy.

Artificial Intelligence (AI)Machine LearningSaaS

No H1B

Responsibilities

Design the framework that supports multiple partnership and customer models: Deep Embedded (white-label components), Best-of-Breed SaaS (standalone platform with APIs), Data Layer Only (predictions via API)

Evaluate architectural tradeoffs across complexity, risk, scalability, time-to-market, and value capture for each pattern

Make build vs. buy decisions: direct API integrations vs. iPaaS middleware vs. embedded agents

Define authentication strategies across OAuth 2.0, certificate-based auth, and federated identity for different customer security models

Create deployment patterns that work across on-premise, cloud, and hybrid environments

Design for portfolio risk

Build bi-directional integrations with customer ERP systems and telematics platforms

Architect event-driven systems that turn predictive alerts into automated workflows

Implement multiple integration patterns (Direct API, middleware/iPaaS, embedded agents, webhooks) to support different partnership and customer models

Transform flat parts catalogs into semantic networks using AWS Neptune

Design ontologies that capture ACES (fitment) and PIES (attributes) standards for heavy equipment

Build ingestion pipelines that parse customer data and extract compatibility relationships

Implement graph traversal algorithms for multi-hop reasoning ("find compatible substitute parts in stock")

Create AI agent orchestration using Amazon Bedrock that breaks complex requests into multi-step workflows

Build tool functions agents invoke: graph queries, customer API calls, inventory checks, order placement

Implement GraphRAG systems that ground LLM responses in structured graph data to prevent hallucination on critical fitment recommendations

Work with founders to evaluate integration architectures across different partnership strategies (deep embedded, best-of-breed SaaS, data layer only)

Make build vs. buy decisions for integration infrastructure

Design authentication and deployment patterns that work across on-premise, cloud, and hybrid environments

Qualification

Graph databasesPythonAPI designEvent-driven architectureSemantic query languagesData normalizationAuthentication modelsTeam leadershipProblem solvingCommunication skills