AI Backend Engineer - Data & Integration

Digital Iron; Boston (MA); On-site; USD 120,000 - 150,000; Full time

Job summary

Digital Iron in Boston is seeking a dedicated AI Infrastructure Engineer to lead the design of integration architectures and build knowledge graph systems for predictive maintenance across the heavy equipment ecosystem.

The ideal candidate will have deep technical expertise in distributed systems, with experience in graph databases and API integrations. This role will require building multi-step workflows and ensuring data accuracy while collaborating on various partnership models.

Qualifications

• Experience with graph databases like Neptune or Neo4j.
• Ability to design bidirectional API integrations for enterprise systems.
• Strong Python skills for data pipelines and application logic.

Responsibilities

• Design integration architecture for customer ERP systems.
• Build knowledge graph systems to transform parts catalogs.
• Implement agentic workflows using Amazon Bedrock.

Skills

Graph databases experience

Ability to model complex domain relationships

Strong Python skills

Experience with event-driven architectures

Knowledge of authentication models

Obsession with data accuracy

Tools

AWS Neptune

Gremlin

SPARQL

Job description

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.

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 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

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. Build ingestion pipelines that parse customer data and extract compatibility relationships. Implement graph traversal algorithms for multi‑hop reasoning.

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 Systems

• Experience with graph databases (Neptune, Neo4j) and ontology design
• Ability to model complex domain relationships as graph structures
• Understanding of semantic query languages (Gremlin, SPARQL) and entity resolution
• 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
• 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)
• 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)

Nice‑to‑Haves

• Experience in automotive, heavy equipment, or industrial IoT domains
• Experience with embedded/white‑label integration models or AI agent frameworks
• A network of sec‑ops and ML compliance resources and colleagues to tap as we scale our team
• Experience 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’re more comfortable with Kubernetes and Terraform than APIs and databases
• 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 (NY, VT, ME, MA, CT, DC, VA, NC, GA only)