Principal Engineer, Customer Engineering

Responsibilities

• Architect end‑to‑end integration solutions for complex enterprise environments spanning ERP (SAP, Oracle, Blue Yonder, Manhattan), OMS, WMS, TMS, and YMS platforms, working directly with customer technical teams to translate business problems into implementable designs
• Design and build data pipelines (batch and streaming) using messaging infrastructure and event‑driven architectures that enable real‑time supply chain data flow at enterprise scale
• Build, extend, and optimize agentic AI workflows using Lang Graph, Lang Chain, and related frameworks, integrating LLM‑powered automation into customer‑facing supply chain operations
• Design microservices architectures for integration layers: API gateway patterns, service decomposition, inter‑service communication, containerization (Docker, Kubernetes), and cloud‑native deployment on AWS, Azure, or GCP
• Develop and maintain APIs (REST, GraphQL) and integration middleware connecting customer enterprise systems to the Four Kites platform
• Own technical proof‑of‑concepts for new integration patterns, AI‑powered workflows, and platform capabilities, taking them from prototype to production‑ready
• Define reference architectures and reusable integration patterns that scale across customers, covering traditional EDI/file‑based integrations (X12, EDIFACT, flat file, SFTP/AS2) through modern event‑driven and AI‑augmented approaches
• Establish engineering standards for data quality, observability, error handling, retry logic, and scalability across all customer integrations
• Drive technical decisions on integration approach selection: real‑time streaming vs. batch ETL, synchronous vs. asynchronous patterns, push vs. pull models, grounded in customer system constraints and business requirements
• Evaluate emerging technologies and integration patterns, building team capability in AI/ML engineering, cloud‑native architectures, and modern data infrastructure
• Build and maintain trusted‑advisor relationships with technical and executive stakeholders at strategic accounts, spanning the full lifecycle from initial scoping through production optimization
• Provide solutions architecture consultation during pre‑sales: scoping complex integration and AI agent deployments, identifying technical risks, sizing effort, and translating platform capabilities into customer business value
• Partner with R&D teams to shape the platform roadmap based on field experience, advocating for integration‑layer and AI capability improvements grounded in real customer implementation data
• Contribute to external thought leadership: reference architectures, technical blog posts, conference presentations, and customer‑facing best practice documentation
• Serve as the senior escalation point for the hardest integration and AI engineering challenges, both internal and customer‑facing
• Mentor customer engineers (onshore and offshore) through design reviews, code reviews, pairing sessions, and architecture walkthroughs, raising the technical bar across the team
• Lead by building: prove out new approaches hands‑on before asking the team to adopt them
• Define and track integration quality and engineering productivity metrics

• 12+ years in software engineering, data engineering, or solutions architecture, with significant time in customer‑facing roles designing and delivering enterprise‑grade systems
• Hands‑on builder: you write production code, build data pipelines, debug distributed systems, and architect solutions, not just review them
• Deep expertise in data pipeline and messaging infrastructure:
  Kafka, RabbitMQ, SQS/SNS, or comparable; hands‑on experience designing streaming and batch data flows at scale
• Strong API engineering skills: designing, building, and scaling REST and GraphQL APIs; experience with API gateway patterns, rate limiting, versioning, and authentication protocols
• Hands‑on AI engineering experience: building agentic workflows and LLM‑powered applications using Lang Graph, Lang Chain, or equivalent frameworks; practical understanding of prompt engineering, tool orchestration, retrieval‑augmented generation, and agent evaluation
• Microservices architecture expertise: service decomposition,…