AI Backend Engineer - Data & Integration

At Digital Iron, we're building the intelligent infrastructure that powers predictive maintenance and parts procurement automation across the heavy equipment ecosystem. We work with customers to transform how industrial equipment is maintained.

We're looking for an AI Infrastructure Engineer who combines deep technical expertise in distributed systems with strategic thinking about integration architecture. You'll need exceptionally high standards for data accuracy, first-principles problem solving, and an obsession with building systems that scale across diverse partnership models.

As our first dedicated infrastructure engineer, you'll work on problems at the intersection of knowledge graphs, real-time IoT data, and enterprise integration—building infrastructure that thousands of businesses will depend on.

• 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

• Design Integration Architecture
• 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.
• Build Knowledge Graph Systems
• 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").
• Develop Agentic Workflows
• 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.

• Graph & Semantic Systems
• Experience with graph databases (Neptune, Neo4j) and ontology design
• Ability to model complex domain relationships as graph structures, not tables
• Understanding of semantic query languages (Gremlin, SPARQL) and entity resolution
• Backend Engineering & Data Systems
• Strong Python for data pipelines, graph operations, and application logic
• Experience with database design across relational and graph paradigms
• Background normalizing data from disparate sources with conflicting formats
• Enterprise Integration & API Design
• Track record designing bidirectional API integrations with enterprise systems
• Experience with event-driven architectures, webhooks, and async workflows
• Knowledge of authentication models (OAuth 2.0, SAML, certificate-based)
• Data Engineering Excellence
• Strong experience normalizing data from disparate sources with conflicting formats
• Obsession with accuracy where 99% is insufficient—compatibility data must be correct
• Experience building automated validation and conflict resolution systems
• Ability to model complex business domains (you'll learn heavy equipment specifics)

• Experience in automotive, heavy equipment, or industrial IoT domains
• Experience with embedded/white-label integration models or AI agent frameworks
• Knowledge of industry standards (ACES, PIES, OAGIS) or similar B2B data formats
• A network of sec-ops and ML compliance resources and colleagues to tap as we scale our team
• TypeScript for backend services and integration middleware
• Experience working with founders to evaluate integration architectures across different partnership strategies (deep embedded, best-of-breed SaaS, data layer only)

• You prefer infrastructure automation over application architecture
• You're more comfortable with Kubernetes and Terraform than APIs and databases
• You need complete requirements before designing systems
• You view integration work as "plumbing" rather than strategic architecture

Leadership & Team - You will have one staff-level engineering direct report with dotted lines across a team of engineers. You will be expected to deliver 80% hands-on code development with 20% oversight across our vendors, strategy and a direct report. We can be flexible on title for the right candidate.

• Location: US (East Coast & Mid-West remote only), South London & Belfast Office (Hybrid)
• Visa: Cannot sponsor at this time
• Start: Immediate availability preferred

If you're excited about designing backend systems at the intersection of graph databases, enterprise integration, and AI agents—where your technical decisions have direct business impact—we want to hear from you.