AI/ML Engineer

  • Pretoria, South Africa
  • Full-Time
  • On-Site
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Job Description:

AI/ML Engineer

Purpose:

Serve as the technical backbone of the AI Hub. Design, build, productionise and maintain core AI/ML infrastructure and reusable components that will power the DT2.0 platform build, priority use cases, and the Group's intelligent transformation.

Key Responsibilities:

  • Design and implement core AI infrastructure — model serving, MLOps pipelines, RAG architectures, vector databases, DT Data Lake integration, rules engine and microservices.
  • Lead technical delivery of high-impact use cases — AI-Assisted Legacy Modernisation, Intelligent Operations, and AI-native components of DT2.0.
  • Build and maintain the foundational AI Agent Layer and reusable AI services across onboarding, operations and sales enablement, including multi-agent orchestration.
  • Apply AI/ML techniques to accelerate the DT2.0 platform build (automated code analysis, intelligent configuration generation, predictive monitoring).
  • Implement production-grade evaluation, observability and monitoring — eval harnesses, regression testing, A/B testing of models, continuous quality measurement.
  • Own LLM cost and latency optimisation — model selection, caching, prompt engineering, token economics, and routing logic.
  • Establish AI security standards — prompt injection defence, output validation, secrets management, model access controls — to fintech-grade requirements.
  • Ensure all solutions are modular, scalable, cloud-native, secure, and fully compliant with the Responsible AI Policy, PCI-DSS and POPIA.
  • Mentor squad members and contribute to AI standards and best practices across the organisation.
  • Track and report technical KPIs and ROI of AI initiatives.

Requirements & Qualifications:

Essential

  • BSc Computer Science or equivalent technical degree — non-negotiable.
  • 3–6 years software engineering experience with AI/ML in production.
  • Strong C#/.NET proficiency (primary API stack), shipping production APIs.
  • Python proficiency for AI/ML workloads.
  • Modern AI/ML frameworks: LangChain, LlamaIndex, Hugging Face, OpenAI/Anthropic APIs, scikit-learn, PyTorch/TensorFlow.
  • RAG systems with vector DBs (Pinecone, Weaviate, pgvector, Qdrant).
  • Agentic / multi-agent systems (LangGraph, CrewAI, AutoGen, custom orchestration).
  • MLOps, Azure (preferred), Docker/Kubernetes, data pipelines.
  • Azure DevOps (primary) for CI/CD.
  • Passion for clean, production-grade code.

Bonus / Advantageous

  • FastAPI for AI service-specific work.
  • Eval / observability tooling — LangSmith, Langfuse, Arize, W&B.
  • Fine-tuning approaches — PEFT, LoRA, QLoRA, and trade-off judgement.
  • GitHub Actions or other modern CI/CD tooling.
  • Modern data engineering — dbt, Spark/Databricks, Kafka.
  • Fintech/payments or large-scale system modernisation background.

What Success Looks Like in 12 Months:

  • Core AI infrastructure is live and reused across multiple DT2.0 squads.
  • Two priority use cases (Legacy Modernisation Toolkit and Operations layer) are in production.
  • Measurable acceleration of DT2.0 delivery velocity, visible to EXCO.
  • AI services in production with defined SLAs, eval coverage, and cost/latency baselines.