Location
Hybrid (Cape Town, South Africa)
Seniority
Staff · 10+ years exp
First seen by Alion on Oct 27, 2026.
Overview
Company
Impact
Profile match
Datafin is a South African recruitment agency that places IT, data and finance professionals with client companies. Its vacancies include senior data analysts, quality engineering leaders, product managers and fund accountants, mainly for firms in Cape Town and Johannesburg. The company is headquartered in Cape Town, South Africa.
ENVIRONMENT:
JOIN the team of an Online Gaming Group seeking to fill the hands-on technical role of an AI Architect/ Engineer. You will serve as one of the site’s most senior individual technical contributors including to its AI-augmented engineering model. The role exists because the transformation the site has committed to, rebuilding how software is specified, written, tested and released around AI assistance, requires deep hands-on engineering ownership that cannot be delivered from a line management position and cannot be bought in as advisory.
DUTIES:
AI Engineering Harnesses:
- Accountable for the design, build, operation and lifecycle of the internal harnesses that make AI-assisted Engineering safe and repeatable: agent and assistant scaffolding, repository and domain context layers, prompt and workflow libraries, evaluation harnesses, regression and guardrail suites, and the integration of these into the Developer toolchain and CI/CD pipeline.
- Decide on the technical design and implementation of these systems.
AI adoption in delivery:
- Accountable for ensuring AI augmentation actually lands in the delivery teams: reference implementations, integration patterns per stack, Developer enablement, and removal of the technical obstacles that stall adoption.
- Work through Heads of Engineering and Engineering Managers, who retain accountability for their teams’ delivery outcomes.
Domain and technical depth:
- Accountable for holding sufficient depth in business domains — player journey, account and wallet, bonusing and promotions, game integration, payments, responsible gambling controls, regulatory reporting — to judge where AI assistance is safe, where it is not, and what a correct output looks like.
- Own the domain context and knowledge artefacts on which AI-assisted work depends.
Technical standards and architecture practice:
- Accountable for Engineering standards governing AI-assisted development: code provenance and attribution, review obligations for generated code, licensing and IP hygiene, secure coding under assistance, and the definition of done for AI-assisted change.
- Set these standards within the group reference architecture and recommends changes to it where augmentation requires them.
Engineering telemetry and measurement:
- Accountable for the technical instrumentation that makes engineering performance observable, delivery, quality and flow telemetry, and the pipelines and analysis layers that turn it into decision-grade evidence for the Managing Director and group stakeholders.
- Determine the technical implementation; the measurement framework and its interpretation are agreed with the Managing Director and Heads of Engineering.
Technical evaluation and vendor assessment:
- Accountable for the technical evaluation of AI platforms, models, tooling and third-party services proposed for engineering use, including capability benchmarking against workloads, security and data-handling posture, cost model and exit risk.
- Recommend to the Managing Director, who holds selection and contracting authority.
Technical leadership without authority:
- Accountable for raising the technical ceiling of the engineering organisation: mentoring Senior and Lead Engineers, leading design and architecture review, setting the standard for engineering craft, and building the internal advocacy that carries the augmentation model past initial resistance.
- Hold no line authority over any Engineer.
Prototyping and technical risk reduction:
- Accountable for de-risking novel technical initiatives ahead of committed delivery through working prototypes and spikes, and for producing a clear, evidenced recommendation to proceed, adapt or stop.
REQUIREMENTS:
Qualifications –
- Bachelor’s Degree in Computer Science, Software Engineering, Engineering or a related field.
Experience/Skills -
- 10+ Years in Software Engineering, with a sustained record of hands-on delivery at senior or staff-plus level.
- Demonstrated ownership of platform, tooling or Developer-experience systems used across an engineering organisation of comparable scale.
- Practical, shipped experience applying AI or Machine Learning to engineering workflows
- Evidence of technical leadership without line authority across multiple teams.
- Delivery accountability in a regulated, consumer-facing, high-availability environment (advantageous).
Desired Skills:
- AI Architect
- Engineer
- CPT
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