- Work directly with business users and Product Managers to design, prototype and engineer reusable data and AI products on Guardrisk's governed data platform, including ML models, AI-ready datasets and agents that automate data work.
What makes this different
- Combines data engineering, software engineering and applied AI
- Works in the business context, understanding how data is created, used and trusted, not only from a technical specification
- Automates the data lifecycle itself, from ingestion and quality to reconciliation and reporting, rather than adding manual workarounds
- Rapidly prototypes data and AI products with users and learns through deployment
- Designs agentic workflows for data and analytics use cases, with orchestration, human-in-the-loop controls and exception handling
- Connects data, models, APIs and existing systems on the governed platform
- Builds reusable data components, semantic models, feature sets, agent skills and tools for CoE use cases
- Makes sound design choices beyond one immediate use case, with reuse across Guardrisk in mind
- Partners with Technology on platform, ETL, security and productionisation
- Establishes the engineering practices, documentation and patterns that make Guardrisk's data AI-ready
- Supports adoption and measures whether deployed products actually improve data trust, speed and use
- Expected seniority: Strong hands-on engineer with enough technical depth to engineer data products, models and agents, and enough product judgement to decide where AI adds value, where deterministic automation is better, and where human judgement or control must remain.
The role should have the potential to grow into broader product or product-architecture leadership.
- Scope boundary: AI and automation in the Data CoE means solutions whose inputs, outputs or runtime sit on Guardrisk's data platform and data products, such as analytics, reporting, data operations, models, insight agents and natural language access to data. Automating business workflows owned by a business unit, such as claims handling, underwriting intake or finance operations, sits outside this role and is coordinated through the CDTO Front Door.
Requirements
Qualifications
We're looking for someone who can operate across data engineering, software and AI rather than specialising in only one of them.
- Bachelor's degree in Computer Science, Engineering, Information Systems or a related technical discipline, or equivalent practical experience.
- Strong software engineering experience in at least one modern programming language, ideally Python, SQL, C# or similar.
- Practical experience building data pipelines, data-quality automation or analytical solutions.
- Experience designing and consuming REST APIs and integrating multiple systems or services.
- Hands-on experience building applications using large language models or generative AI APIs.
- Understanding of agentic concepts such as tool use, orchestration, retrieval, structured outputs, state and human-in-the-loop patterns.
- Experience taking solutions beyond prototypes into production or operational use.
- Ability to work directly with non-technical users to understand how they use data and translate that into a technical solution.
- Strong engineering fundamentals, including source control, testing, documentation, security and maintainability.
- Ability to communicate technical trade-offs clearly to both business and technical stakeholders.
You don't need to have experience with every technology below. We're particularly interested in people who have gone deep enough in this space to understand what works in production and what does not.
- Hands-on experience developing AI agents, multi-agent or agentic workflow solutions, or ML models on enterprise data.
- Experience with agent frameworks, orchestration approaches or modern AI engineering toolchains.
- Experience with Databricks, Azure data services, lakehouse architecture or equivalent data platforms.
- Experience with Azure Data Factory, Logic Apps or Power Automate for data pipeline orchestration and alerting.
- Experience with the Microsoft Azure and Microsoft 365 ecosystem, particularly where AI and enterprise data intersect.
- Experience applying techniques such as retrieval-augmented generation (RAG), vector search, structured extraction into governed datasets and ML operations (MLOps).
- Experience with data modelling, semantic layers, data-quality frameworks, lineage or data cataloguing.
- Experience designing reusable data platforms, components, libraries or engineering patterns rather than only delivering one-off solutions.
- Experience implementing evaluation, observability or monitoring for ML and AI applications.
- Understanding of responsible AI, POPIA, privacy, security and appropriate controls for using data in AI solutions.
- Experience in financial services, insurance or another regulated environment.
- Evidence of being a builder and capability creator: someone comfortable starting with an ambiguous data problem, working with users, building something quickly and progressively turning it into a production-quality product.
Duties & Responsibilities
About the role
- We're building practical Data & AI engineering capability within Guardrisk's Data CoE, and we're looking for a hands-on engineer who wants to help shape how AI and automation work on our data.
- This is not a role focused on producing AI demos or building technology in isolation. You will work directly with business users and Product Managers to understand how data is created and used, find where data work is slow, manual or untrusted, and turn those opportunities into working data and AI products.
- You'll combine data engineering, software engineering and applied AI to build everything from automated pipelines, quality checks and reconciliations to ML models, AI-ready datasets and agents that automate data work.
- Just as importantly, you'll help us build the capability itself: reusable components, patterns and engineering practices that let AI solutions across Guardrisk run on trusted data.
- Guardrisk's Data CoE is built around reusable data products, a governed platform and business-facing delivery. Technology builds and runs the platform, and the CoE builds data and AI products on it. The role focuses on AI and automation on Guardrisk's data platform and data products.
- Automation of business workflows owned by individual business units is led elsewhere in Guardrisk.
- Work directly with business users and Product Managers to understand, map and document how data flows through business processes.
- Identify manual data handling, quality gaps, reconciliation breaks and repetitive reporting that can be automated or augmented using AI.
- Translate data problems into practical data automation, ML and agentic AI solutions.
- Rapidly prototype with users, test ideas in the real business context and turn successful prototypes into robust solutions.
- Design and build AI agents and agentic workflows that reason over governed data, use tools and APIs, check quality, reconcile sources, document datasets and answer questions over certified data products.
- Apply LLMs, retrieval-augmented generation, tool use and structured extraction to CoE use cases, with evaluation built in from the start.
- Build conventional pipeline and reporting automations where deterministic automation is a better fit than AI.
- Integrate solutions with the governed data platform and with source systems, APIs and data services.
- Design appropriate human-in-the-loop controls, approvals, exception handling and escalation paths.
- Develop reusable semantic models, feature sets, data components and agent skills for data tasks rather than solving every problem from scratch.
- Work with Technology, Architecture, Security and other teams to take solutions from prototype through to controlled production use.
- Build evaluation and monitoring into models, agents and data automations so that we understand whether they are working as intended and delivering value.
- Document solutions, patterns and lessons learned so that the capability becomes repeatable across the Data CoE.
- Prepare AI-ready data, including curated, certified and retrieval-ready datasets, so that AI and automation solutions across Guardrisk consume trusted data rather than building their own.
- Contribute to the standards and engineering practices that underpin Guardrisk's data platform and data products.
- AI-ready, certified data products and curated datasets
- Automated data pipelines, data-quality checks and reconciliations
- AI agents and multi-step agentic workflows for data, analytics and reporting use cases
- Reusable semantic models, feature sets and data components
- Human-in-the-loop and exception-handling patterns for data automation
- Working prototypes and MVPs
- ML models and analytical applications
- Evaluation, monitoring and operational controls for models and agents
- Deployment-ready code and technical documentation
- Reusable data and AI engineering patterns and standards
- Knowledge transfer that grows data, analytics and ML capability across the CoE and extended studio teams
Deadline:5th November,2026