Xephyr

Full-stack Engineer

• Design, build and deploy production software across Lessn's payments orchestration platform
• Integrate with card networks, banks, the acquirer and payout rails
• Contribute to the AI/ML decision engine that optimises invoice payment using real-time open banking data
• Build and maintain reconciliation integrations with Xero, MYOB and future ERP platforms
• Introduce and champion CI/CD, automated testing and infrastructure-as-code practices
• Mentor the graduate engineer, review code and support development
• Act as a senior quality gate before production release in a regulated fintech environment
• Participate in a shared escalation roster and serve as technical escalation point for customer-impacting incidents
• Feed delivery insights into the product roadmap across integrations, accounting connectors, Payday Super, multi-currency payments and real-time reconciliation
• Complete platform migration, compliance work, deployment ownership, automated testing, deployment pipeline, infrastructure rebuild, supplier verification/KYB automation and the data foundation/decision engine

Senior Engineer

• Develop and execute AI strategies to achieve business objectives and expand AI capabilities
• Translate business and technical objectives into data-engineering requirements
• Communicate architecture decisions, trade-offs, and roadmap progress
• Lead end-to-end project management for data engineering initiatives
• Manage commercial, technical, and executive stakeholders
• Mentor junior engineers and champion best practices in code reviews, CI/CD, and documentation
• Adapt to changing toolkits, models, scopes, POCs, and prototypes
• Collaborate with sales, marketing, product, and delivery teams
• Support team recruitment and expansion
• Design end-to-end systems combining models, workflows, automation, and APIs
• Build data ingestion, transformation, and feature-store pipelines powering production LLMs and ML models
• Embed automated data-quality tests, monitoring, and cost controls
• Lead AI initiatives addressing real-world business needs
• Integrate AI models with workflow automation and decision systems
• Design and implement batch and streaming ELT/ETL workflows
• Integrate APIs, events, files, warehouses, and optimize cloud storage and compute
• Design, build, and maintain scalable data pipelines, data models, and production-grade machine-learning solutions
• Introduce repeatable and scalable AI processes