Software Engineer (AI & Automation)

AI Use Cases Automation Delivery

• Identify and prioritize AI/automation opportunities, map current vs. future processes, and define clear business requirements across functions (e.g., exception handling, document processing, service operations, billing/audit, reporting)
• Develop and configure AI prototypes/solutions using modern AI tools and platforms (e.g., LLM apps/agents, RAG, document processing), then iterate into production-ready delivery with internal IT/engineering teams
• Help identify where workflow automation/low-code tools can streamline processes (e.g., approvals, data analysis, exception management) and support requirement definition, testing, and rollout
• Hands-on build automations where appropriate; validate solution quality through testing and UAT support with clear documentation
• Apply AI for document processing, data extraction, classification, decision support, and workflow optimization; ensure solutions are measurable (cycle time, accuracy, cost savings)
• Run discovery with business stakeholders, design, build, and ship AI-powered applications end-to-end — from prototype to production
• Ensure responsible AI and operational readiness: data privacy, access control, auditability, monitoring, and clear handover/support procedures System Integration (Enterprise Applications Data)
• Build and maintain system integrations between AI solutions and enterprise systems (e.g. finance/accounting platforms, and—where applicable—TMS/WMS etc.) via APIs, files, or EDI
• Work with internal IT/security teams to ensure appropriate access control, data privacy, and compliance requirements are met for each solution Cloud, Data AI Platform Skills
• Familiarity with cloud and AI platforms used to deliver enterprise solutions (Azure preferred; other major clouds are a plus)
• Familiarity with low-code/workflow tools (e.g., Power Platform); ability to translate business needs into configurable solutions matters most Collaboration, Training Change Management
• Partner with business team to identify opportunities and prioritize AI/automation backlog based on business value
• Create and maintain technical documentation, SOPs, and user guides; ensure solutions are supportable.
• Run knowledge-transfer and training sessions to drive adoption; communicate changes and collect feedback for continuous improvement