Full Stack Engineer

Russell Tobin; Sydney; On-site; AUD 120,000 - 180,000; Full time

Job summary

Russell Tobin is seeking a senior AI/Full Stack Engineer to design, build and deploy AI-powered solutions for production environments in Sydney. You will develop and maintain LLM-based applications, implement RAG architectures, and build AI agents to automate document-centric processes.

The role requires hands-on experience with LLMs, prompt engineering, model evaluation, and DevSecOps practices in enterprise settings.

Qualifications

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

Responsibilities

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

Skills

AI deployment experience

Prompt engineering

RAG

AI agents and orchestration

Model performance optimization

Enterprise AI integration

AI governance & responsible AI

Document classification & OCR

Structured/unstructured data handling

Automation for outcomes

Tools

Docker

AWS cloud engineering

CI/CD pipelines

APIs & integration

Observability tooling

Job description

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

Technical Engineering Experience

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

Key Responsibilities :

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

Full Stack Engineering

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

Cloud, Platform & DevSecOps

• Build and operate cloud-native solutions on AWS.
• Develop containerised workloads using Docker.
• Design and enhance CI/CD pipelines to improve delivery speed and quality.
• Automate infrastructure, testing and deployment activities wherever possible.
• Improve observability, operational resilience and platform reliability.
• Apply modern DevSecOps practices throughout the software delivery lifecycle.