AI Solution Architect

Candidate Description

Specialising in enterprise, solution and domain architecture, the candidate brings extensive experience across agentic AI, machine learning, payments, supply chain, retail, manufacturing and aviation. Their career includes architecture assignments with a consulting firm, an airline, a retail group, a beverage company, a tobacco company and a technology consulting firm. They translate business strategy into target architectures, transition roadmaps and practical delivery guidance across hybrid and multi-cloud environments. Core strengths include API-led integration, event-driven microservices, data architecture, enterprise governance and secure AI adoption. Their background combines customer-facing digital transformation with operational platforms supporting payments, workforce planning, promotions and supply-chain digitisation. They communicate complex architectural choices through clear documentation, structured options assessments and business-focused recommendations.

Agentic AI delivery is a particular strength, demonstrated through work at a consulting firm on LangChain, LangGraph, Anthropic Claude and production-grade retrieval-augmented generation. The candidate defined document-ingestion, embedding, vector-search, reranking and response-generation architectures, together with multi-agent supervision and human-in-the-loop controls. At an airline, they authored the enterprise ML platform assessment and led AWS SageMaker architecture and implementation for Workforce Planning optimisation. That work incorporated Teradata integration, event-driven ingestion, CI/CD, model monitoring, bias detection, access governance and cost controls. For a retail group, they shaped the retailer's payment-gateway re-platforming around microservices, Kafka, API governance and resilient payment orchestration. Their approach combines Agile and SAFe knowledge with architecture review governance, lifecycle controls and objective production-readiness criteria.

Architectural leadership is reinforced by close collaboration with business sponsors, product owners, engineers, DevOps specialists and risk and compliance teams. The candidate mentors delivery teams while maintaining a clear distinction between architectural direction and hands-on implementation responsibilities. Their experience spans technology selection, operating-model definition, stakeholder approval, staged deployment and post-go-live governance. A Master of Engineering from a university complements professional credentials including CBAP, PRINCE2 Practitioner and AWS Certified AI Practitioner. They bring particular value to organisations seeking secure, scalable AI capabilities that integrate with established enterprise applications and data landscapes. Based in Sydney, they are an Australian citizen available for contract opportunities in two weeks.