Job Description

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- Demonstrated success deploying AI solutions that generated measurable business outcomes.
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- Hands-on experience with Large Language Models such as OpenAI, Claude, Gemini, Llama or equivalent.
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- Hands-on experience with prompt engineering, Retrieval Augmented Generation (RAG), AI agents and agentic workflows.
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- Experience with model orchestration frameworks, AI evaluation frameworks and model performance optimization.
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- Experience integrating LLMs into enterprise applications and business workflows.
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- Understanding of AI security, privacy, explainability, fairness, governance and responsible AI controls.
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- Experience with document classification, data extraction, OCR technologies, validation and verification frameworks, document workflow automation, redaction services and document intelligence platforms.
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- Demonstrated ability to improve customer and operational outcomes through intelligent automation.
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- Experience processing structured, semi-structured and unstructured document workloads.
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Technical Engineering Experience

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- Strong programming capability in one or more of Python, Java, Go or .NET.
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- Strong API design and integration experience.
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- Strong AWS cloud engineering capability.
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- Docker and containerization experience.
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- CI/CD pipeline design and implementation experience.
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- Automated testing framework experience.
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- Experience working in highly regulated enterprise environments.
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- Robust software design and solution architecture skills.
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- Deep understanding of DevSecOps principles.
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- Experience designing resilient and observable systems.
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- Strong problem-solving and systems-thinking capabilities.
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- Demonstrated ability to balance innovation, delivery, risk and resilience.
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Key Responsibilities :

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- Design, build and deploy AI-powered solutions into production environments.
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- Develop and maintain LLM-based applications that deliver measurable business value.
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- Design and implement Retrieval Augmented Generation (RAG) architectures.
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- Build AI agents and orchestration frameworks that automate document-centric business processes.
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- Develop prompt engineering, model evaluation and model optimisation capabilities.
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- Assess and select appropriate AI models based on business outcomes, performance, cost and risk considerations.
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- Drive adoption of AI-powered engineering practices across the crew.
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- Design and build enterprise-scale Intelligent Document Processing solutions.
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- Develop document classification, extraction, validation, redaction and workflow automation capabilities.
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- Integrate OCR, document intelligence and AI technologies into customer journeys.
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- Improve straight-through processing rates through intelligent automation.
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- Build reusable document intelligence services that can be leveraged across multiple business domains.
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- Drive innovation in document processing through emerging AI technologies.
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Full Stack Engineering

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- Design, develop and maintain modern web applications and APIs.
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- Build responsive user experiences using ReactJS.
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- Develop backend services using Python, Java, Go or .NET.
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- Design scalable microservices and event-driven architectures.
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- Contribute to technical architecture, engineering standards and strategic technology direction.
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- Mentor and uplift engineering capability across the squad and broader engineering community.
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Cloud, Platform & DevSecOps

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- Build and operate cloud-native solutions on AWS.
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- Develop containerised workloads using Docker.
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- Design and enhance CI/CD pipelines to improve delivery speed and quality.
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- Automate infrastructure, testing and deployment activities wherever possible.
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- Improve observability, operational resilience and platform reliability.
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- Apply modern DevSecOps practices throughout the software delivery lifecycle.
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📌 Full Stack Engineer (New South Wales)
🏢 Russell Tobin
📍 New South Wales