AI Engineer Intern (JHB Hybrid)

DataFin was established in 1999. We pride ourselves on the fact that we have established relationships with industry leaders and a vast majority of our business is repeat business.

Duties & Responsibilities

ENVIRONMENT: JOIN an applied-AI work on a structured, mentored pathway, rotating through the data engineering 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 internship, 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.

Desired Experience & Qualification

AI, Engineer, Intern, JHB, Hybrid,