Senior/ IT Consultant (AI Platform)

SIT’s AI Platform is the institution’s enterprise AI platform and Centre of Excellence that accelerates the development, deployment and operation of AI solutions across the university. It provides shared AI platform capabilities, reusable products and assets, governance and safety controls, and expert guidance that enable teams to deliver AI solutions more rapidly while maintaining appropriate levels of security, compliance, reliability and operational oversight. The AI Platform supports a broad spectrum of AI builders, including citizen developers, low-code/ no-code practitioners, software developers, AI engineers and solution teams. Through reusable AI services, models, LLM endpoints, agent runtimes, vector and knowledge services, MCP services, APIs, SDKs, tools, templates, reference architectures and implementation patterns, teams are able to focus on solving business problems while leveraging common institutional capabilities. The AI Platform also embeds governance, safety and Responsible AI practices into the platform itself through guardrails, observability, explainability, traceability, cost accountability and compliance controls. This ensures that AI solutions are trustworthy, auditable and compliant by design, while enabling adoption at scale across SIT. Reporting to the Lead IT Consultant (AI Platform), the Senior/ IT Consultant (AI Platform) is responsible for designing, building, operating and continuously improving the reusable services, products and engineering assets that make up the AI Platform. The role is expected to be the primary pro-code builder within the platform team, with strong software engineering and Python capability, and to work hands-on across platform services, reusable APIs, SDKs, tools, integrations and implementation patterns that accelerate AI solution delivery across SIT. Key Responsibilities: AI Platform Engineering and Shared Services • Design, build and maintain the core technical capabilities of the AI Platform, including models, LLM endpoints, agent runtimes, vector and knowledge services, model routers and other shared platform services. • Provide a stable, scalable and reusable technical foundation that teams can rely on to build and run AI solutions. Reusable Products – e.g. Agent harness, memory & context, Agent loops, MCP & Tools, agent orchestration and SDK • Design, build, and operate the core AI platform components, including LLM inference endpoints, embedding services, vector database infrastructure, and agent orchestration layers. Ensure these services are production-ready, secure, scalable, and continuously maintained, with appropriate monitoring, reliability engineering, and operational processes in place to support enterprise-wide usage. • Reduce duplication, improve consistency and enable teams to build faster using common platform capabilities and proven engineering patterns. Platform Operations, Reliability and Optimisation • Operate and continuously improve the AI Platform’s reliability, performance, scalability and maintainability through sound engineering practices, operational monitoring, troubleshooting and performance optimisation. • Ensure that the AI Platform remains production-ready, supportable and fit for sustained institutional use. Continuous Improvement and Technical Innovation • Continuously improve the AI Platform by refining existing services, exploring new engineering approaches, strengthening developer experience and identifying opportunities to enhance reuse, automation and platform effectiveness. • Keep the AI Platform effective, modern and responsive to emerging engineering needs and technology developments. Governance-by-Design and Engineering Standards • Work with the AI Governance Engineer to embed governance, safety, security and operational requirements into the design, build and operation of platform services, reusable assets and engineering patterns. • Ensure that platform capabilities are secure, governed and compliant by design, while remaining usable and scalable for teams across SIT. Requirements: • Degree in Computer Science, Information Systems, Engineering, Information Technology or a related discipline. • Relevant experience in software engineering, application development, platform engineering or AI platform delivery, with hands-on coding and integration experience • Strong software engineering and development skills, with the ability to design, build and maintain reusable platform components and application integrations. • Strong hands-on coding ability in Python, with familiarity in building APIs, services, scripts or automation relevant to AI platform delivery on Cloud. • Good understanding of AI platform components, including models, LLM services, agent harness and loop, memory and context, APIs, SDKs, vector or knowledge services and reusable implementation patterns. • Able to engineer reusable products, tools, services and patterns that support platform adoption across multiple teams and use cases. • Good understanding of cloud platforms, integration patterns, application architecture and operational considerations for shared platform services. • Able to support production-grade platform operations, troubleshooting, optimization and continuous improvement. • Good understanding of engineering standards, governance-by-design and secure delivery practices for AI platforms. • Able to work effectively with platform, governance and solution teams to deliver reusable and governed platform capabilities.