AI & Data Engineer

AI & Data Engineer

We are looking for an AI and Data Engineer who can operate across the full

delivery stack – from cloud infrastructure and data pipelines through to LLM

application development and client-facing requirements discovery.

This is a hands-on role for someone who builds things, ships fast, and is

comfortable with ambiguity. You will work across multiple client engagements

simultaneously, own your workstreams end to end, and contribute to a

growing technical practice.

JOB DESCRIPTION INCLUDES

1. Cloud Infrastructure & DevOps

• Provision and manage cloud infrastructure on AWS or Azure. Deploy and

maintain environments for active client engagements. Own your

environments – do not wait for someone else to unblock you.

• Write infrastructure as code using Terraform or equivalent. All

environments are version-controlled, reproducible, and deployable

without manual steps.

• Containerise applications using Docker. Implement CI/CD pipelines

using Azure DevOps, GitHub Actions, or equivalent.

• Apply identity, access, and networking fundamentals across all

environments. IAM, RBAC, SAS tokens, VPC configuration. Security is not

optional.

2. Data Engineering & Integration

• Design and implement data pipelines and orchestration workflows.

Work across SQL, data modelling, and Python. Handle structured and

unstructured data from multiple sources.

• Build and integrate APIs using REST, FastAPI, or equivalent. Connect

disparate client systems using appropriate integration patterns.

• Work with messy, undocumented client data and make it useful. Apply

data quality and transformation approaches that are pragmatic and

fast rather than perfect and slow.

3. LLM & AI Application Development

• Build LLM-enabled applications: prompting, RAG architectures,

embeddings, and evaluation frameworks. Use open-source and

commercial LLM providers as the use case demands.

• Design and implement RAG systems for document-heavy

environments. Handle chunking, embedding, vector store selection, and

retrieval quality evaluation end to end.

• Build evals and testing frameworks for LLM outputs. AI application

behaviour must be measurable and reproducible.

• Apply prompting best practices and LLM application patterns

appropriate to the use case. Do not over-engineer. The measure of

success is a working system in front of the client.

• Document AI application architecture so the client team can operate it

after you leave. Handover is part of the job.

4. Consulting Delivery

• Take problem briefs from the Managing Partner or Technical Lead and

turn them into working solutions. Not everything will be clearly defined.

Asking the right questions, scoping tightly, and delivering something

demonstrable is the job.

• Scope a POC down to the smallest provable increment. Prioritise rapid

prototyping over polished engineering. Speed of iteration matters more

than perfection in the early stages.

• Estimate accurately and hit short timelines. Communicate blockers

early to the Technical Lead. If something is going to be late, say so

before it is too late to do anything about it.

• Contribute to a growing technical practice. Share what you learn,

document what you build, and help make the next engagement faster

than the last one.

TECHNICAL REQUIREMENTS

Formal qualifications are not the primary criterion. We want to see evidence

of delivery. Show us what you have built.

Core technical stack

• Cloud: AWS or Azure — at least one deeply; compute, storage,

networking, identity

• Infrastructure as Code: Terraform or equivalent

• Containerisation: Docker, basic orchestration

• Languages: Python — data wrangling, scripting, glue code — and SQL

You should also have

• Data pipelines and orchestration — you have built them and they ran in

production

• APIs and integration patterns — REST, FastAPI, or equivalent

• LLM application work — prompting, RAG, embeddings, evals. Not just

experimented with. Built something.

• CI/CD — Azure DevOps, GitHub Actions, or equivalent

• Observability and debugging in environments you did not build

WHAT WE ARE LOOKING FOR

• Someone who is comfortable with ambiguity. Client requirements are

often vague, data is often messy, and timelines are always short. That

needs to excite you, not frustrate you.

• A track record of scoping and shipping POCs and MVPs quickly. We

value speed of learning and delivery over years of experience.

• Someone who communicates proactively. Blockers get raised early.

Nothing is hidden until the deadline.

• A genuine interest in the practical application of AI – not the hype. You

read the papers and you also ship the code.

• Based in or able to work aligned to South African business hours. The

role is remote-first with occasional on-site requirements.

TECHNICAL SKILLS

• Programming: Python, JavaScript frameworks — Next.js, React

advantageous — SQL

• Frameworks & Tools: Node.js, PyTorch, Docker, Kubernetes

• Data & Integration: RESTful APIs, microservices architecture, message

queues

• DevOps & Version Control: Git/GitHub, CI/CD pipelines

• Systems: Windows and Linux environments

• Cloud Platforms: AWS, Azure, or GCP advantageous

CORE COMPETENCIES

• Strong problem-solving, analytical, and system-thinking capability

• Ability to design scalable, maintainable, and secure software solutions

• Strong communication skills with technical and non-technical

stakeholders

• High levels of ownership, accountability, and professional judgement

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