Data & AI Product Engineer Gauteng

BRIEF DESCRIPTION:-

Data & AI Product Engineer

This role combines expertise in data engineering, software engineering, and applied AI to design, prototype, and engineer reusable data and AI products on a governed data platform. Key responsibilities include working with business users to understand data flows, identifying manual data handling gaps, and translating data problems into practical data automation, ML, and agentic AI solutions. The ideal candidate has a strong software engineering background, practical experience with data pipelines and AI, and the ability to communicate technical trade-offs to both business and technical stakeholders.

**Job Title: Data & AI Product Engineer**

**Company: Guardrisk**

**Role Purpose**

We are seeking a highly skilled Data & AI Product Engineer to join our Data Centre of Excellence (CoE) at Guardrisk. As a key member of our team, you will play a critical role in designing, prototyping, and engineering reusable data and AI products on our governed data platform. This includes developing ML models, AI-ready datasets, and agents that automate data work, while ensuring the highest standards of trust, speed, and use.

**What Makes This Role Unique**

This role combines expertise in data engineering, software engineering, and applied AI to drive business outcomes. You will work closely with business users and Product Managers to understand how data is created, used, and trusted, and translate that understanding into practical data automation, ML, and agentic AI solutions. You will automate the data lifecycle itself, from ingestion and quality to reconciliation and reporting, rather than adding manual workarounds.

**Key Responsibilities**

• 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

**Requirements**

• 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

**Preferred Qualifications**

• 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

**Competencies**

• 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