Ai Enablement Lead

Focus Kamoso (Pty) Ltd; Gauteng; On-site; ZAR 200 000 - 300 000; Full time

Job summary

A leading financial services company in Gauteng seeks an AI Enablement Lead to transform data science into robust production systems. The role involves guiding a cross-functional squad to deliver machine learning and LLM models, ensuring operational excellence, and aligning delivery with strategic objectives. Ideal candidates have a Master's degree and 12+ years in the field, along with strong skills in Python and cloud technologies. This position offers the chance to make a significant impact on AI innovations.

Qualifications

• 12+ years' experience in software engineering, data engineering, or AI productionisation.
• Postgraduate qualification in AI, Data Science, or Systems Engineering is advantageous.
• Deep understanding of agile methodologies and team leadership.

Responsibilities

• Lead and mentor a diverse team in AI delivery.
• Own the technical roadmap for ML and LLM production.
• Implement operational processes for production AI systems.
• Collaborate with data science and infrastructure teams.
• Drive improvements in AI delivery velocity.

Skills

Advanced proficiency in Python

SQL

Cloud-native development

MLOps/LLMOps tools

CI/CD

Docker

Kubernetes

Infrastructure-as-Code

Education

Master's degree in Computer Science, Engineering, or related field

Tools

Vertex AI

BigQuery

Cloud Composer

Kubeflow

Job description

Company Summary

A leading JSE-listed financial services company is committed to improving people's lives and delivering forward-thinking innovations across the healthcare and financial ecosystem.

The organisation thrives on curiosity, high performance, and the pursuit of meaningful change.

It offers a dynamic environment where exceptional talent collaborates to create solutions with long‑term impact.

About the Group Data Science Team

The Group Data Science Team is expanding and plays a central role in shaping digital, clinical, wellness and behavioural solutions across the business.

The team works with large-scale structured and unstructured data on modern cloud and big‑data architectures, collaborating with global partners and academic institutions to develop high-impact AI solutions.

With a future‑fit platform and a focus on new data opportunities, the team builds scalable, production‑ready systems that support strategic business priorities.

Requirements

• Master's degree in Computer Science, Engineering, or a related field.
• 12+ years' experience in software engineering, data engineering, or AI productionisation.
• Advanced proficiency in Python, SQL, cloud‑native development, and MLOps / LLMOps tools.
• Strong experience with CI / CD, Docker, Kubernetes, and Infrastructure‑as‑Code.

Advantageous

• Postgraduate qualification in AI, Data Science, or Systems Engineering.
• Experience with Vertex AI, BigQuery, Cloud Composer, Kubeflow, or similar platforms.

Attributes

• Collaborative mentor with a passion for developing others.
• Pragmatic, delivery‑focused, and solutions‑driven.
• Strong communicator with the ability to simplify complex concepts.
• Curious, innovative, and adaptable with an ownership mindset.

Role Summary: AI Enablement Lead

The AI Enablement Lead is responsible for transforming advanced data science into robust, scalable production systems.

The role guides a multidisciplinary engineering squad and drives the productionisation of machine learning and LLM models.

This includes designing production‑grade systems, ensuring engineering and operational excellence, and aligning technical delivery with strategic business objectives.

Responsibilities

• Team Leadership & Delivery
  • Lead and mentor a cross‑functional squad including engineers, developers, analysts, and data scientists.
  • Drive agile delivery practices to ensure high‑quality, on‑time deployment of AI solutions.
• Technical Strategy & Architecture
  • Own the technical roadmap for ML and LLM productionisation.
  • Oversee architectural decisions for model deployment, data pipelines, and cloud‑native systems.
• Operational Excellence
  • Implement monitoring, alerting, observability, and incident response processes for production AI systems.
  • Champion best practices in CI / CD, testing, automation, and reliability engineering.
• Stakeholder Collaboration
  • Work with data science teams to convert prototypes into stable, production‑ready applications.
  • Partner with platform and infrastructure teams to ensure seamless integration and scalability.
• Strategic Impact
  • Drive enterprise‑wide improvement in AI delivery velocity and reliability.
  • Represent the AI Enablement Squad in strategic forums and contribute to group‑wide AI innovation.