AI Backend Engineer - Data & Integration

Job DescriptionAt 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. What You'll Own 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 patternMake build vs. buy decisions: direct API integrations vs. iPaaS middleware vs. embedded agentsDefine authentication strategies across OAuth 2.0, certificate-based auth, and federated identity for different customer security modelsCreate deployment patterns that work across on-premise, cloud, and hybrid environmentsDesign for portfolio risk What You'll Do 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. What We're Looking For Graph & Semantic SystemsExperience with graph databases (Neptune, Neo4j) and ontology designAbility to model complex domain relationships as graph structures, not tablesUnderstanding of semantic query languages (Gremlin, SPARQL) and entity resolution Backend Engineering & Data SystemsStrong Python for data pipelines, graph operations, and application logicExperience with database design across relational and graph paradigmsBackground normalizing data from disparate sources with conflicting formats Enterprise Integration & API DesignTrack record designing bidirectional API integrations with enterprise systemsExperience with event-driven architectures, webhooks, and async workflowsKnowledge of authentication models (OAuth 2.0, SAML, certificate-based) Data Engineering ExcellenceStrong experience normalizing data from disparate sources with conflicting formatsObsession with accuracy where 99% is insufficient-compatibility data must be correctExperience building automated validation and conflict resolution systemsAbility to model complex business domains (you'll learn heavy equipment specifics) Nice-to-HavesExperience in automotive, heavy equipment, or industrial IoT domainsExperience with embedded/white-label integration models or AI agent frameworksKnowledge of industry standards (ACES, PIES, OAGIS) or similar B2B data formatsA network of sec-ops and ML compliance resources and colleagues to tap as we scale our teamTypeScript for backend services and integration middlewareExperience working with founders to evaluate integration architectures across different partnership strategies (deep embedded, best-of-breed SaaS, data layer only) This Role Is NOT For You If:You prefer infrastructure automation over application architectureYou're more comfortable with Kubernetes and Terraform than APIs and databasesYou need complete requirements before designing systemsYou view integration work as "plumbing" rather than strategic architecture Role Level 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. Role Details

Location: US (East Coast & Mid-West remote only), South London & Belfast Office (Hybrid)Visa: Cannot sponsor at this timeStart: Immediate availability preferred Ready to Build Integration & Data Systems That Matters?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.