AI Engineer (JHB Hybrid)
SUMMARY:
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POSITION INFO:
ENVIRONMENT: JOIN an applied-AI work on a structured, mentored pathway, rotating through the that feeds it as the next AI Engineer wanted by a provider of tailored Financial Solutions. You will prepare trusted data for AI workloads, help run an in-house governed AI platform, and test AI-assisted use cases under supervision and strictly inside policy. This is an engineering-and-governance , not a research post, and you will not be training bespoke models. What you will learn is how AI gets used safely and usefully on real, sensitive data in a regulated business. Interns who grow well here are first in line as the team expands, and the combined Data & AI Engineer role is the named destination on the team's growth path. Candidates will require foundational Python, whether from coursework, projects, or self-teaching, but you must be able to read, run, and modify a script & a basic understanding of SQL and relational data concepts. DUTIES: · Prepare, clean, validate, and document the datasets that AI and search workloads consume. · Support the in-house governed AI platform: content ingestion, indexing, configuration, and routine health checks under supervision. · Test and evaluate AI-assisted use cases such as summarisation, classification, search, extraction, and automation against defined success criteria. · Write small Python scripts and notebooks to automate repetitive data preparation and evaluation tasks. · Record evaluation evidence: what was tested, what the AI produced, what a human reviewer changed, and why. · Apply data-minimisation and anonymisation rules without exception, which means no personal information, credentials, or production data goes into unapproved tools. · Contribute to documentation: use-case write-ups, prompt and configuration notes, data dictionaries, and testing evidence. · Learn the company governance, security, privacy, and change management standards from day one. · Attend team ceremonies and give clear, honest progress updates on assigned tasks. What you’ll work with – · A governed in-house AI platform, where you will learn to use AI tools properly, with controls, from week one. · Python, notebooks, and Git, giving you modern engineering habits from the start. · SQL Server and Microsoft Fabric, the governed data estate that feeds every AI use case. · Power BI, which is how results reach the business. · Evaluation and human-review workflows, which are how we decide whether an AI output is good enough to use. REQUIREMENTS: · Foundational Python, whether from coursework, projects, or self-teaching, but you must be able to read, run, and modify a script. · Basic understanding of SQL and relational data concepts. · Practical curiosity about AI tools, paired with healthy scepticism about their output. · Attention to detail and willingness to document work clearly. · Good written and verbal communication, and genuine appetite to learn. · Awareness that data is sensitive and that confidentiality is non-negotiable. Advantageous - · Qualification in progress or recently completed in Data Science, Computer Science, Information Systems, Statistics, Mathematics, Engineering, or related. · Exposure to APIs, notebooks, retrieval \/ vector search concepts, or any LLM tooling. · Any project where you used AI to solve a real problem, especially if you can explain where it got things wrong. · Portfolio, coursework, hackathon, or capstone evidence of data or AI problem-solving. Show us anything you have built. · Basic awareness of responsible-AI concerns: bias, hallucination, privacy, and traceability. ATTRIBUTES: · You verify before you trust, and that includes AI output above all. · You learn fast and enjoy it, because new tools and feedback energise you rather than intimidate you. · You can explain what a tool actually did, not just that it seemed to work. · You check your work and ask when unsure, with no silent guessing. · You want a career in data or AI, not just a gap-filler job. · Integrity and values, with the ability to handle sensitive and confidential information. · Attention to detail and a results-driven quality mindset. · Strong problem-solving; composure in a fast-moving, dynamic environment. · Works well in a team and independently; communicates openly. · Growth mindset, actively wanting to learn and develop.